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CORE: Improving Compositional Reasoning in MLLM Embedding via Reranker Distillation
artificial-intelligencecomputation-and-languagecomputer-vision-and-pattern-recognitioninformation-retrieval
MLLM-based embedding models remain limited in compositional retrieval, often failing to distinguish scenes containing the same concepts but different attribute-object bindings. Yet the same backbone can resolve such distinctions when used as a cross-attentive reranker, motivating us to distill its c
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