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About this paper
Qiyuan Wang, Shang Zhao, Zikang Xu, S Kevin Zhou
The LACOSTE framework enhances surgical instrument segmentation by systematically integrating stereo and temporal contexts with a novel method to decouple location bias from classification. It demonstrates superior segmentation and classification accuracy across three surgical video benchmarks, addressing challenging scenarios like occlusion and motion blur.
Key insights
- Systematically combining stereo and temporal contexts within a query-based segmentation framework significantly improves surgical instrument segmentation robustness and accuracy.
- Decoupling location-dependent mask prediction features from location-agnostic classification features leads to more semantically coherent object queries and enhanced classification.
- Effective pseudo-identity matching and tracklet augmentation can leverage temporal and stereo cues for consolidated predictions, even without explicit video instance annotations.
Summary by alphaXiv.
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