SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation (2007.14772v1)

Abstract: Single-stage instance segmentation approaches have recently gained popularity due to their speed and simplicity, but are still lagging behind in accuracy, compared to two-stage methods. We propose a fast single-stage instance segmentation method, called SipMask, that preserves instance-specific spatial information by separating mask prediction of an instance to different sub-regions of a detected bounding-box. Our main contribution is a novel light-weight spatial preservation (SP) module that generates a separate set of spatial coefficients for each sub-region within a bounding-box, leading to improved mask predictions. It also enables accurate delineation of spatially adjacent instances. Further, we introduce a mask alignment weighting loss and a feature alignment scheme to better correlate mask prediction with object detection. On COCO test-dev, our SipMask outperforms the existing single-stage methods. Compared to the state-of-the-art single-stage TensorMask, SipMask obtains an absolute gain of 1.0% (mask AP), while providing a four-fold speedup. In terms of real-time capabilities, SipMask outperforms YOLACT with an absolute gain of 3.0% (mask AP) under similar settings, while operating at comparable speed on a Titan Xp. We also evaluate our SipMask for real-time video instance segmentation, achieving promising results on YouTube-VIS dataset. The source code is available at https://github.com/JialeCao001/SipMask.

Overview of SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation

The paper proposes SipMask, a novel single-stage instance segmentation method focused on preserving spatial information within detected bounding boxes to achieve both fast performance and high mask accuracy. The primary objective of this research is to bridge the gap between the speed of single-stage methods and the accuracy of two-stage approaches commonly seen in instance segmentation tasks.

Core Contributions

1. Novel Spatial Preservation Module: The paper introduces a Spatial Preservation (SP) module that generates spatial coefficients by dividing detected bounding boxes into sub-regions. This approach helps preserve instance-specific spatial information that is typically lost in other single-stage methods. The spatial coefficients allow finer delineation of spatially adjacent instances, enhancing the quality of the mask predictions.
2. Mask Alignment Weighting Loss: To improve correlation between mask prediction and object detection, the researchers propose a mask alignment weighting loss. This loss gives higher importance to mask prediction errors arising from accurately detected boxes, thus ensuring that better quality masks are produced for well-localized objects.
3. Feature Alignment Scheme: The feature alignment scheme introduced in the SP module enhances feature representation by aligning features with the final regressed bounding-box locations. This results in improved mask accuracy and helps ensure spatial integrity is maintained in the mask predictions.
4. Improvements Over Existing Methods: When evaluated on the COCO dataset, SipMask demonstrated considerable increases in mask accuracy compared to existing single-stage methods such as YOLACT and TensorMask, while maintaining a significant advantage in inference speed. Specifically, SipMask achieves a four-fold speed improvement over TensorMask with a comparable mask accuracy, and even outperforms YOLACT in terms of accuracy while maintaining similar speeds.

Implications and Speculative Remarks

The SipMask method has substantial implications for real-time applications. Its ability to operate at real-time speeds while achieving state-of-the-art accuracy suggests potential utility in fields requiring fast, accurate object detection and segmentation, such as autonomous driving, video surveillance, and augmented reality.

The use of spatial coefficients within detected bounding boxes introduces a paradigm shift in single-stage designs, presenting opportunities for future work to explore multi-scale spatial configuration settings and adaptive learning strategies for spatial encoding based on object characteristics.

Further development could include expanding the SipMask framework to other vision tasks such as 3D instance segmentation, where spatial delineation is critical. Moreover, its application in video instance segmentation highlights its scalability and adaptability, pointing to possibilities in long-term temporal tracking scenarios in dynamic environments.

In conclusion, SipMask represents a significant advancement in single-stage instance segmentation, demonstrating that through careful spatial information preservation, it is possible to achieve high levels of both speed and accuracy. This work lays the groundwork for future improvements in both practical applications and theoretical understanding of spatial relationships in segmentation tasks.