RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation

Abstract: Object detection and instance segmentation are two fundamental computer vision tasks. They are closely correlated but their relations have not yet been fully explored in most previous work. This paper presents RDSNet, a novel deep architecture for reciprocal object detection and instance segmentation. To reciprocate these two tasks, we design a two-stream structure to learn features on both the object level (i.e., bounding boxes) and the pixel level (i.e., instance masks) jointly. Within this structure, information from the two streams is fused alternately, namely information on the object level introduces the awareness of instance and translation variance to the pixel level, and information on the pixel level refines the localization accuracy of objects on the object level in return. Specifically, a correlation module and a cropping module are proposed to yield instance masks, as well as a mask based boundary refinement module for more accurate bounding boxes. Extensive experimental analyses and comparisons on the COCO dataset demonstrate the effectiveness and efficiency of RDSNet. The source code is available at https://github.com/wangsr126/RDSNet.

• The paper presents a reciprocal learning framework that integrates object-level and pixel-level streams to boost detection and segmentation accuracy.
• It introduces novel correlation and cropping modules to generate sharper instance masks and refine boundary localization.
• Experiments on the COCO dataset show RDSNet achieves a superior balance between real-time processing and precise detection and segmentation.

Overview of RDSNet: A New Deep Architecture for Object Detection and Instance Segmentation

The paper presents RDSNet, a novel deep learning architecture designed for enhancing the performance of two fundamental computer vision tasks: object detection and instance segmentation. The motivation behind this work lies in the close relationship between these tasks, which has not been fully explored or exploited in many previous approaches. By introducing a reciprocal learning framework, RDSNet aims to improve performance by facilitating information flow between object-level and pixel-level representations.

Key Contributions and Methodology

RDSNet adopts a two-stream structure, consisting of an object stream and a pixel stream. The object stream focuses on extracting object-level features, such as bounding boxes, through a regression-based detector, while the pixel stream leverages these features to predict instance-aware segmentation masks using a fully convolutional network (FCN) architecture. The reciprocal flow of information between these streams is a distinctive aspect of the framework.

The framework employs two novel modules:

1. Correlation Module: This module facilitates the transition from instance-agnostic to instance-aware segmentation by determining the correlation between object representations and pixel representations. This method ensures that pixels are correctly associated with their respective object instances.
2. Cropping Module: This module mitigates the inherent weaknesses of conventional mask generation strategies, notably the risk of noise incorporation due to convolution's translation-invariance. Cropping with expanded bounding boxes is introduced to decouple mask dependency on detection results. The paper shows that this strategy improves mask resolution and reduces noise involvement, albeit with additional computational complexity.

Furthermore, the paper introduces a Mask Based Boundary Refinement Module (MBRM), which refines object detectors by revisiting boundary localization using pixel-level information, capitalizing on learned instance masks to deliver more accurate bounding boxes.

Experimental Results

The paper reports extensive analyses on the COCO dataset, demonstrating that RDSNet achieves superior performance compared to existing competitive approaches in both instance segmentation and object detection metrics. For instance segmentation, RDSNet exhibits a consistent balance between high accuracy and real-time processing speeds, situating it favorably against both one-stage and two-stage state-of-the-art models. For object detection, the addition of a mask generator and MBRM distinctly bolsters the bounding box localization precision.

Implications and Future Directions

The proposed reciprocal approach sets a strong precedent for future research on joint task frameworks in computer vision. The ability of RDSNet to alleviate drawbacks such as low mask resolution and strong dependency on bounding boxes suggests promising applications in scenarios demanding high-resolution perception, such as autonomous driving and robotics.

Theoretically, the paper broadens the scope of multi-task learning by highlighting potential interactions between tasks traditionally viewed in isolation. RDSNet's success encourages further exploration into other perceivable correlations among diverse vision-based tasks, potentially leading to holistic architectures that advance the field of artificial intelligence.

Future work could explore the application of RDSNet principles to other domains where task reciprocity can yield performance improvements, or refine the model through exploring alternative backbone architectures or more sophisticated correlation strategies. The implications of integrating such a framework with large pre-trained models or real-time systems are also intriguing avenues for continued research.