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About this paper
Soham Walimbe, Britty Baby, Vinkle Srivastav, Nicolas Padoy
A new multi-task framework, MML-SurgAdapt, leverages Vision-Language Models (VLMs) and Single Positive Multi-Label (SPML) learning to unify diverse surgical computer vision tasks. This approach enables handling phase recognition, safety assessment, and action recognition within a single model while significantly reducing annotation requirements by 23% compared to traditional multi-label setups, achieving competitive performance.
Key insights
- Vision-Language Models can be effectively adapted and fine-tuned for specialized multi-task surgical computer vision, moving towards unified AI systems.
- Single Positive Multi-Label (SPML) learning, especially with Hill Loss, proves highly robust in integrating diverse surgical tasks despite pervasive partial annotations.
- A substantial reduction in annotation burden (23% fewer labels) can be achieved without compromising performance, making surgical AI more practical.
Summary by alphaXiv.
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