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About this paper
Yueming Jin, Yonghao Long, Cheng Chen, Zixu Zhao, Qi Dou, Pheng-Ann Heng
A Temporal Memory Relation Network (TMRNet) is presented for surgical workflow recognition from video, developed by researchers at The Chinese University of Hong Kong. This end-to-end framework effectively integrates long-range and multi-scale temporal information, achieving up to 90.1% accuracy and 79.1% Jaccard index on the Cholec80 dataset, outperforming prior methods that relied on shorter temporal contexts.
Key insights
- Explicitly integrating long-range temporal information via an auxiliary memory bank significantly enhances surgical workflow recognition, providing more substantial performance gains than multi-scale temporal modeling alone.
- Modeling multi-scale temporal variations through diverse kernel sizes in a dedicated layer allows the network to adaptively understand the complex temporal dynamics of surgical actions.
- An attentive non-local operator can effectively fuse historical, multi-scale context with current visual features within an end-to-end framework, outperforming simpler fusion strategies and enabling real-time application.
Summary by alphaXiv.
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