Optimize Surgical Triplet Recognition: A Knowledge-Driven Mixture-of-Experts Solution
Surgical action triplet recognition constitutes a critical task in context-aware robot-assisted surgery, facilitating automatic surgical action perception by identifying instrument, verb, target, and their association. However, existing works struggle to analyze such complex surgical scenes due to three main issues: (1) component-level optimization conflicts caused by entangled feature spaces, (2) category-level optimization conflicts arising from severe data imbalance, and (3) lack of domain knowledge guidance that limits model interpretability and robustness. To address these challenges, we propose a Mixture-of-Experts-guided Co-Optimization (\textit{MoeCo}) framework powered by knowled...
| Authors | Yiyi Zhang, Yuchen Yuan, Ying Zheng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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