6 citations · 15 across the 11 of their papers we have counts for
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cs.CV2024
Debiased Orthogonal Boundary-Driven Efficient Noise Mitigation
Hao Li, Jiayang Gu, Jingkuan Song +2
Mitigating the detrimental effects of noisy labels on the training process has become increasingly critical, as obtaining entirely clean or human-annotated samples for large-scale…
cs.CV2023
Negative Pre-aware for Noisy Cross-modal Matching
Xu Zhang, Hao Li, Mang Ye
Cross-modal noise-robust learning is a challenging task since noisy correspondence is hard to recognize and rectify. Due to the cumulative and unavoidable negative impact of unreso…
cs.CV2023★ 6 cited
Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval
Hao Li, Jingkuan Song, Lianli Gao +2
Cross-modal Retrieval methods build similarity relations between vision and language modalities by jointly learning a common representation space. However, the predictions are ofte…