A Survey of Visual Transformers

Abstract: Transformer, an attention-based encoder-decoder model, has already revolutionized the field of NLP. Inspired by such significant achievements, some pioneering works have recently been done on employing Transformer-liked architectures in the computer vision (CV) field, which have demonstrated their effectiveness on three fundamental CV tasks (classification, detection, and segmentation) as well as multiple sensory data stream (images, point clouds, and vision-language data). Because of their competitive modeling capabilities, the visual Transformers have achieved impressive performance improvements over multiple benchmarks as compared with modern Convolution Neural Networks (CNNs). In this survey, we have reviewed over one hundred of different visual Transformers comprehensively according to three fundamental CV tasks and different data stream types, where a taxonomy is proposed to organize the representative methods according to their motivations, structures, and application scenarios. Because of their differences on training settings and dedicated vision tasks, we have also evaluated and compared all these existing visual Transformers under different configurations. Furthermore, we have revealed a series of essential but unexploited aspects that may empower such visual Transformers to stand out from numerous architectures, e.g., slack high-level semantic embeddings to bridge the gap between the visual Transformers and the sequential ones. Finally, three promising research directions are suggested for future investment. We will continue to update the latest articles and their released source codes at https://github.com/liuyang-ict/awesome-visual-transformers.

• The paper presents a comprehensive review of visual Transformers, detailing their innovative attention-based architectures in key computer vision tasks.
• It organizes over 100 models into a clear taxonomy by tasks and data types, including distinctions like patch- and query-based approaches in segmentation.
• The paper demonstrates significant performance gains over CNNs on benchmarks such as ImageNet and COCO, while outlining future directions for efficient training and self-supervised learning.

A Survey of Visual Transformers

The integration of Transformer architectures into computer vision tasks marks a significant advancement beyond their original utility in natural language processing. The paper provides an extensive survey of visual Transformers across fundamental computer vision (CV) tasks including classification, detection, and segmentation, as well as their application to diverse data types such as images, point clouds, and vision-language data.

Overview of Visual Transformers

Visual Transformers have gained traction by incorporating attention mechanisms that enable them to capture long-range dependencies inherent in CV tasks. The paper organizes more than one hundred models into a clear taxonomy based on their applications, comparing architectures, motivations, and their effectiveness across different vision tasks. The impact on these tasks is measured against state-of-the-art convolutional neural networks (CNNs), highlighting significant improvements in performance and modeling capability.

Key Contributions

1. Comprehensive Review: The survey delineates the broad landscape of visual Transformers, focusing on their structure and design rationale across different CV tasks. It discusses various architectural feats such as hierarchical, deep, and self-supervised visual Transformers, providing insights into their unique contributions and performance metrics.
2. Taxonomy of Visual Transformers: The paper classifies visual Transformers by task and data type, aiding in understanding the diverse approaches and their applications in CV. For instance, for segmentation, the models are organized into patch-based and query-based Transformers, further dissecting into object queries and mask embeddings.
3. Performance Analysis: Detailed performance analysis is presented, showing how visual Transformers achieve substantial accuracy gains over CNNs, especially with large datasets in classification tasks. The paper provides visual and tabular comparisons on tasks such as ImageNet classification and COCO detection, elucidating their efficiency and scalability.
4. Future Directions: The paper highlights potential research directions, advocating for further exploration into set prediction in classification, self-supervised learning paradigms, and more efficient training strategies to mitigate limitations such as longer convergence times and computational complexity.

Implications and Speculation on Future Developments

Visual Transformers hold promise for unifying multiple CV tasks within a coherent architectural framework, leveraging their strength to synthesize learning across different sensory streams and task requirements. This unification could facilitate end-to-end training models without the need for handcrafted components like anchor boxes in object detection or bounding box proposals in segmentation. In the field of AI, broader adoption of visual Transformers could lead to more generalizable and robust models capable of performing a multitude of tasks with less data-specific tweaking.

The survey underscores the importance of visual Transformers in advancing CV tasks and suggests that with continuous research, they could become a cornerstone technology for future AI systems, akin to what CNNs achieved in the earlier deep learning wave.