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Learning Multi-axis Representation in Frequency Domain for Medical Image Segmentation
Published 28 Dec 2023 in eess.IV and cs.CV | (2312.17030v2)
Abstract: Recently, Visual Transformer (ViT) has been extensively used in medical image segmentation (MIS) due to applying self-attention mechanism in the spatial domain to modeling global knowledge. However, many studies have focused on improving models in the spatial domain while neglecting the importance of frequency domain information. Therefore, we propose Multi-axis External Weights UNet (MEW-UNet) based on the U-shape architecture by replacing self-attention in ViT with our Multi-axis External Weights block. Specifically, our block performs a Fourier transform on the three axes of the input features and assigns the external weight in the frequency domain, which is generated by our External Weights Generator. Then, an inverse Fourier transform is performed to change the features back to the spatial domain. We evaluate our model on four datasets, including Synapse, ACDC, ISIC17 and ISIC18 datasets, and our approach demonstrates competitive performance, owing to its effective utilization of frequency domain information.
- Berseth M (2017) Isic 2017-skin lesion analysis towards melanoma detection. arXiv preprint arXiv:170300523 Cao et al (2021) Cao H, Wang Y, Chen J, et al (2021) Swin-unet: Unet-like pure transformer for medical image segmentation. arXiv preprint arXiv:210505537 Chen et al (2021) Chen J, Lu Y, Yu Q, et al (2021) Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:210204306 Chen et al (2022) Chen J, He T, Zhuo W, et al (2022) Tvconv: Efficient translation variant convolution for layout-aware visual processing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 12,548–12,558 Chen et al (2023) Chen Q, Cai S, Cai C, et al (2023) Colo-scrl: Self-supervised contrastive representation learning for colonoscopic video retrieval. arXiv preprint arXiv:230315671 Codella et al (2019) Codella N, Rotemberg V, Tschandl P, et al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:190203368 Dosovitskiy et al (2020a) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Cao H, Wang Y, Chen J, et al (2021) Swin-unet: Unet-like pure transformer for medical image segmentation. arXiv preprint arXiv:210505537 Chen et al (2021) Chen J, Lu Y, Yu Q, et al (2021) Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:210204306 Chen et al (2022) Chen J, He T, Zhuo W, et al (2022) Tvconv: Efficient translation variant convolution for layout-aware visual processing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 12,548–12,558 Chen et al (2023) Chen Q, Cai S, Cai C, et al (2023) Colo-scrl: Self-supervised contrastive representation learning for colonoscopic video retrieval. arXiv preprint arXiv:230315671 Codella et al (2019) Codella N, Rotemberg V, Tschandl P, et al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:190203368 Dosovitskiy et al (2020a) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Chen J, Lu Y, Yu Q, et al (2021) Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:210204306 Chen et al (2022) Chen J, He T, Zhuo W, et al (2022) Tvconv: Efficient translation variant convolution for layout-aware visual processing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 12,548–12,558 Chen et al (2023) Chen Q, Cai S, Cai C, et al (2023) Colo-scrl: Self-supervised contrastive representation learning for colonoscopic video retrieval. arXiv preprint arXiv:230315671 Codella et al (2019) Codella N, Rotemberg V, Tschandl P, et al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:190203368 Dosovitskiy et al (2020a) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Chen J, He T, Zhuo W, et al (2022) Tvconv: Efficient translation variant convolution for layout-aware visual processing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 12,548–12,558 Chen et al (2023) Chen Q, Cai S, Cai C, et al (2023) Colo-scrl: Self-supervised contrastive representation learning for colonoscopic video retrieval. arXiv preprint arXiv:230315671 Codella et al (2019) Codella N, Rotemberg V, Tschandl P, et al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:190203368 Dosovitskiy et al (2020a) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Chen Q, Cai S, Cai C, et al (2023) Colo-scrl: Self-supervised contrastive representation learning for colonoscopic video retrieval. arXiv preprint arXiv:230315671 Codella et al (2019) Codella N, Rotemberg V, Tschandl P, et al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:190203368 Dosovitskiy et al (2020a) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Codella N, Rotemberg V, Tschandl P, et al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:190203368 Dosovitskiy et al (2020a) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
- Cao H, Wang Y, Chen J, et al (2021) Swin-unet: Unet-like pure transformer for medical image segmentation. arXiv preprint arXiv:210505537 Chen et al (2021) Chen J, Lu Y, Yu Q, et al (2021) Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:210204306 Chen et al (2022) Chen J, He T, Zhuo W, et al (2022) Tvconv: Efficient translation variant convolution for layout-aware visual processing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 12,548–12,558 Chen et al (2023) Chen Q, Cai S, Cai C, et al (2023) Colo-scrl: Self-supervised contrastive representation learning for colonoscopic video retrieval. arXiv preprint arXiv:230315671 Codella et al (2019) Codella N, Rotemberg V, Tschandl P, et al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:190203368 Dosovitskiy et al (2020a) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Chen J, Lu Y, Yu Q, et al (2021) Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:210204306 Chen et al (2022) Chen J, He T, Zhuo W, et al (2022) Tvconv: Efficient translation variant convolution for layout-aware visual processing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 12,548–12,558 Chen et al (2023) Chen Q, Cai S, Cai C, et al (2023) Colo-scrl: Self-supervised contrastive representation learning for colonoscopic video retrieval. arXiv preprint arXiv:230315671 Codella et al (2019) Codella N, Rotemberg V, Tschandl P, et al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:190203368 Dosovitskiy et al (2020a) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Chen J, He T, Zhuo W, et al (2022) Tvconv: Efficient translation variant convolution for layout-aware visual processing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 12,548–12,558 Chen et al (2023) Chen Q, Cai S, Cai C, et al (2023) Colo-scrl: Self-supervised contrastive representation learning for colonoscopic video retrieval. arXiv preprint arXiv:230315671 Codella et al (2019) Codella N, Rotemberg V, Tschandl P, et al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:190203368 Dosovitskiy et al (2020a) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Chen Q, Cai S, Cai C, et al (2023) Colo-scrl: Self-supervised contrastive representation learning for colonoscopic video retrieval. arXiv preprint arXiv:230315671 Codella et al (2019) Codella N, Rotemberg V, Tschandl P, et al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:190203368 Dosovitskiy et al (2020a) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Codella N, Rotemberg V, Tschandl P, et al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:190203368 Dosovitskiy et al (2020a) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. 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In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. 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Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 12,548–12,558 Chen et al (2023) Chen Q, Cai S, Cai C, et al (2023) Colo-scrl: Self-supervised contrastive representation learning for colonoscopic video retrieval. arXiv preprint arXiv:230315671 Codella et al (2019) Codella N, Rotemberg V, Tschandl P, et al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:190203368 Dosovitskiy et al (2020a) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Chen J, He T, Zhuo W, et al (2022) Tvconv: Efficient translation variant convolution for layout-aware visual processing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 12,548–12,558 Chen et al (2023) Chen Q, Cai S, Cai C, et al (2023) Colo-scrl: Self-supervised contrastive representation learning for colonoscopic video retrieval. arXiv preprint arXiv:230315671 Codella et al (2019) Codella N, Rotemberg V, Tschandl P, et al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:190203368 Dosovitskiy et al (2020a) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Chen Q, Cai S, Cai C, et al (2023) Colo-scrl: Self-supervised contrastive representation learning for colonoscopic video retrieval. arXiv preprint arXiv:230315671 Codella et al (2019) Codella N, Rotemberg V, Tschandl P, et al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:190203368 Dosovitskiy et al (2020a) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Codella N, Rotemberg V, Tschandl P, et al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:190203368 Dosovitskiy et al (2020a) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. 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Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
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In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Codella N, Rotemberg V, Tschandl P, et al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:190203368 Dosovitskiy et al (2020a) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. 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Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. 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In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Codella N, Rotemberg V, Tschandl P, et al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:190203368 Dosovitskiy et al (2020a) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. 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In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
- Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020a) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Dosovitskiy et al (2020b) Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. 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In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. 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In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
- Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020b) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:201011929 Gao et al (2023) Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. 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In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
- Gao J, Ruan J, Xiang S, et al (2023) Lamm: Label alignment for multi-modal prompt learning. arXiv preprint arXiv:231208212 Gao et al (2022) Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
- Gao Y, Zhou M, Liu D, et al (2022) A multi-scale transformer for medical image segmentation: Architectures, model efficiency, and benchmarks. arXiv preprint arXiv:220300131 Huang et al (2021) Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Huang Y, Zhou C, Chen L, et al (2021) Medical frequency domain learning: Consider inter-class and intra-class frequency for medical image segmentation and classification. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 897–904 Karimi et al (2021) Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Karimi D, Vasylechko SD, Gholipour A (2021) Convolution-free medical image segmentation using transformers. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. 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In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer, pp 78–88 Landman et al (2015) Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. 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In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
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In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Landman B, Xu Z, Igelsias J, et al (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p 12 Li et al (2023) Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. 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In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
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In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. 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In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
- Li Y, Yu Z, Xiang S, et al (2023) Av-tad: Audio-visual temporal action detection with transformer. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 1–5 Loshchilov and Hutter (2016) Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
- Loshchilov I, Hutter F (2016) Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983 Loshchilov and Hutter (2017) Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:171105101 Milletari et al (2016) Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), IEEE, pp 565–571 Oktay et al (2018) Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. 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Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. 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Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
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In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
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In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Oktay O, Schlemper J, Folgoc LL, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999 Rao et al (2021) Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. 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In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
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In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. 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In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
- Rao Y, Zhao W, Zhu Z, et al (2021) Global filter networks for image classification. Advances in Neural Information Processing Systems 34:980–993 Ronneberger et al (2015) Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 Ruan et al (2022) Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. 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ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
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Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xiang S, Xie M, et al (2022) Malunet: A multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1150–1156 Ruan et al (2023) Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. 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ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. 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ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
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In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
- Ruan J, Xie M, Gao J, et al (2023) Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 481–490 Sandler et al (2018) Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Sandler M, Howard A, Zhu M, et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520 Wang et al (2022a) Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. 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ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
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ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. 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In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
- Wang H, Cao P, Wang J, et al (2022a) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 2441–2449 Wang et al (2022b) Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Cao P, Wang J, et al (2022b) Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, pp 2441–2449 Wang et al (2022c) Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
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In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. 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ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. 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Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. 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ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. 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In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
- Wang H, Xie S, Lin L, et al (2022c) Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp 2390–2394 Wei et al (2021) Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
- Wei J, Hu Y, Zhang R, et al (2021) Shallow attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 699–708 Wu and He (2018) Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Wu Y, He K (2018) Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 Xiang et al (2020) Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Liu T (2020) Progressive learning with style transfer for distant domain adaptation. IET Image Processing 14(14):3527–3535 Xiang et al (2022) Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. 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In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. 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In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
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ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. 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ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
- Xiang S, You G, Li L, et al (2022) Rethinking illumination for person re-identification: A unified view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4731–4739 Xiang et al (2023a) Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Chen H, Ran W, et al (2023a) Deep multimodal representation learning for generalizable person re-identification. Machine Learning pp 1–19 Xiang et al (2023b) Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
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ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
- Xiang S, Fu Y, Guan M, et al (2023b) Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification. Machine Learning 112(6):1923–1940 Xiang et al (2023c) Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
- Xiang S, Qian D, Gao J, et al (2023c) Rethinking person re-identification via semantic-based pretraining. ACM Transactions on Multimedia Computing, Communications and Applications 20(3):1–17 Xiang et al (2023d) Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Xiang S, Qian D, Guan M, et al (2023d) Less is more: Learning from synthetic data with fine-grained attributes for person re-identification. ACM Transactions on Multimedia Computing, Communications and Applications 19(5s):1–20 You et al (2023) You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
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- You D, Xia P, Chen Q, et al (2023) Autokary2022: A large-scale densely annotated dateset for chromosome instance segmentation. arXiv preprint arXiv:230315839 Yushkevich et al (2006) Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
- Yushkevich PA, Piven J, Cody Hazlett H, et al (2006) User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128 Zhang et al (2021) Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
- Zhang Y, Liu H, Hu Q (2021) Transfuse: Fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 14–24 Zhong et al (2022) Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
- Zhong Y, Li B, Tang L, et al (2022) Detecting camouflaged object in frequency domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4504–4513 Zhou et al (2018) Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11 Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
- Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, p 3–11
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