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About this paper
Yiyi Zhang, Yuchen Yuan, Ying Zheng, Jialun Pei, Jinpeng Li, Zheng Li, Pheng-Ann Heng
A Mixture-of-Experts-guided Co-Optimization (MoeCo) framework was developed to improve surgical action triplet recognition by addressing hierarchical optimization conflicts and integrating multimodal large language model (MLLM)-derived knowledge. It achieved an average AP_IVT of 42.6% on CholecT45 and 40.5% on CholecT50, outperforming existing state-of-the-art methods.
Key insights
- Integrating MLLM-generated, fine-grained structural-functional knowledge of surgical instruments through a Mixture-of-Experts mechanism significantly enriches visual representations, enhancing recognition accuracy.
- Parameter-efficient prompt learning with temporal and spatial prompting can effectively create task-specific features, disentangling component-level optimization objectives and resolving gradient conflicts.
- A gradient rebalancing strategy that specifically targets positive and negative gradients for head and tail classes enables more coordinated learning and improves performance on underrepresented categories.
Summary by alphaXiv.
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