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Cross-modal retrieval with noisy correspondence

Noisy correspondence learning aims to eliminate the negative impact of the mismatched pairs (e.g., false positives/negatives) instead of annotation errors in several tasks.

Papers

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Showing 1–10 of 20 papers

TitleStatusHype
ReCon: Enhancing True Correspondence Discrimination through Relation Consistency for Robust Noisy Correspondence LearningCode1
PC^2: Pseudo-Classification Based Pseudo-Captioning for Noisy Correspondence Learning in Cross-Modal RetrievalCode1
UGNCL: Uncertainty-Guided Noisy Correspondence Learning for Efficient Cross-Modal MatchingCode1
Mitigating Noisy Correspondence by Geometrical Structure Consistency LearningCode1
Breaking Through the Noisy Correspondence: A Robust Model for Image-Text Matching—0
Learning with Noisy Correspondence—0
Cross-modal Retrieval with Noisy Correspondence via Consistency Refining and MiningCode1
NAC: Mitigating Noisy Correspondence in Cross-Modal Matching Via Neighbor Auxiliary Corrector—0
REPAIR: Rank Correlation and Noisy Pair Half-replacing with Memory for Noisy Correspondence—0
Learning to Rematch Mismatched Pairs for Robust Cross-Modal RetrievalCode1
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