Papers › Deep Evidential Learning with Noisy Correspondence for Cross-Modal Retrieval
Deep Evidential Learning with Noisy Correspondence for Cross-Modal Retrieval
Yang Qin, Dezhong Peng, Xi Peng, Xu Wang, Peng Hu
Cross-modal retrieval has been a compelling topic in the multimodal community. Recently, to mitigate the high cost of data collection, the co-occurred pairs (e.g., image and text) could be collected from the Internet as a large-scaled cross-modal dataset, e.g., Conceptual Captions. However, it will unavoidably introduce noise (i.e., mismatched pairs) into training data, dubbed noisy correspondence. Unquestionably, such noise will make supervision information unreliable/uncertain and remarkably degrade the performance. Besides, most existing methods focus training on hard negatives, which will amplify the unreliability of noise. To address the issues, we propose a generalized Deep Evidential Cross-modal Learning framework (DECL), which integrates a novel Cross-modal Evidential Learning paradigm (CEL) and a Robust Dynamic Hinge loss (RDH) with positive and negative learning. CEL could capture and learn the uncertainty brought by noise to improve the robustness and reliability of cross-modal retrieval. Specifically, the bidirectional evidence based on cross-modal similarity is first modeled and parameterized into the Dirichlet distribution, which not only provides accurate uncertainty estimation but also imparts resilience to perturbations against noisy correspondence. To address the amplification problem, RDH smoothly increases the hardness of negatives focused on, thus embracing higher robustness against high noise. Extensive experiments are conducted on three image-text benchmark datasets, i.e., Flickr30K, MS-COCO, and Conceptual Captions, to verify the effectiveness and efficiency of the proposed method. The code is available at \urlhttps://github.com/QinYang79/DECL.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections