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MLLMs can be distilled into lightweight arbiters that dramatically improve the robustness of composed image retrieval by disentangling noisy training signals.
Achieve state-of-the-art hybrid nearest neighbor search by explicitly modeling and mitigating data heterogeneity, a problem often overlooked in existing approaches.
