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LaST outperforms traditional models by harnessing the strengths of both large and small architectures, achieving unprecedented accuracy in zero-shot surgical phase recognition.
Dynamic integration of multimodal knowledge boosts the recognition of rare surgical actions, overcoming critical optimization conflicts in robot-assisted surgery.
Stop training all your data the same way: this new method adaptively reweights samples based on how "competitive" they are within a group, leading to better performance and robustness.
Over-reliance on neighborhood similarity in source-free domain adaptation hurts performance; ProCal offers a way to dynamically calibrate predictions and improve generalization.
