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Found 4 papers, 2 papers with code
A Unified Optimal Transport Framework for Cross-Modal Retrieval with Noisy Labels
Despite the remarkable performance of previous supervised CMR methods, much of their success can be attributed to the well-annotated data.
Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval
To achieve this, we propose L2RM, a general framework based on Optimal Transport (OT) that learns to rematch mismatched pairs.
Noisy Correspondence Learning with Meta Similarity Correction
Despite the success of multimodal learning in cross-modal retrieval task, the remarkable progress relies on the correct correspondence among multimedia data.
Noise-Tolerant Learning for Audio-Visual Action Recognition
To address this challenge, we use the audio-visual action recognition task as a proxy and propose a noise-tolerant learning framework to find anti-interference model parameters against both noisy labels and noisy correspondence.
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