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EgoPressureDiff not only outperforms traditional methods but also effectively resolves visual-physical ambiguities in grasp pressure estimation for complex 3D interactions.
By rebalancing proposal distributions, this method sets a new benchmark in few-shot object detection, achieving significant performance gains without added latency.
