QuRe trains composed image retrieval models with a pairwise reward objective on hard negatives found between sharp relevance-score drops, and adds a human-preference benchmark for evaluating retrieval relevance.
Composed image retrieval with text feedback via multi-grained uncertainty regularization
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QuRe: Query-Relevant Retrieval through Hard Negative Sampling in Composed Image Retrieval
QuRe trains composed image retrieval models with a pairwise reward objective on hard negatives found between sharp relevance-score drops, and adds a human-preference benchmark for evaluating retrieval relevance.