2 citations · 6 across the 6 of their papers we have counts for
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Disentangled Noisy Correspondence Learning
Zhuohang Dang, Minnan Luo, Jihong Wang +6
Cross-modal retrieval is crucial in understanding latent correspondences across modalities. However, existing methods implicitly assume well-matched training data, which is impract…
A Unified Optimal Transport Framework for Cross-Modal Retrieval with Noisy Labels
Haochen Han, Minnan Luo, Huan Liu +1
Cross-modal retrieval (CMR) aims to establish interaction between different modalities, among which supervised CMR is emerging due to its flexibility in learning semantic category…
Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval
Haochen Han, Qinghua Zheng, Guang Dai +2
Collecting well-matched multimedia datasets is crucial for training cross-modal retrieval models. However, in real-world scenarios, massive multimodal data are harvested from the I…
Noisy Correspondence Learning with Meta Similarity Correction
Haochen Han, Kaiyao Miao, Qinghua Zheng +1
Despite the success of multimodal learning in cross-modal retrieval task, the remarkable progress relies on the correct correspondence among multimedia data. However, collecting su…
Noise-Tolerant Learning for Audio-Visual Action Recognition
Haochen Han, Qinghua Zheng, Minnan Luo +3
Recently, video recognition is emerging with the help of multi-modal learning, which focuses on integrating distinct modalities to improve the performance or robustness of the mode…
TDLS: A Top-Down Layer Searching Algorithm for Generating Counterfactual Visual Explanation
Cong Wang, Haocheng Han, Caleb Chen Cao
Explanation of AI, as well as fairness of algorithms' decisions and the transparency of the decision model, are becoming more and more important. And it is crucial to design effect…