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Contextual contamination in few-shot vision-language learning is mitigated by a novel semantic purification and reinforcement-learning gating mechanism, unlocking superior performance.
Make your prompts 5x more interpretable without hurting accuracy: IPL combines discrete token selection with continuous optimization, and it's plug-and-play with existing methods.
By strategically deferring ambiguous image regions to human experts, DeferredSeg achieves more trustworthy medical image segmentation than standalone models.