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STD is proposed, a hierarchical token pruning framework that adapts token selection mechanisms to the functional role of each network stage, and introduces a Stability-Adaptive Trigger in deep layers to execute pruning only during semantically stable phases.
Bridging the gap between domains with tailored noise and selective inpainting can revolutionize few-shot object detection performance.