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FUSE achieves a remarkable 9.1% improvement in mAP by leveraging mid and high-frequency features, challenging the conventional focus on low-frequency cues in multi-modal ReID.
Larger sliding-window attention can paradoxically slow down the formation of critical retrieval mechanisms in language models, challenging conventional design assumptions.
Personality induction boosts image captioning but can hinder reasoning tasks, revealing a complex interplay in MLLM behavior that demands tailored approaches.
