Noise Self-Correction via Relation Propagation for Robust Cross-Modal Retrieval

Cross-modal retrieval refers to identifying semantically relevant data across different modalities. However, annotation errors or inherent ambiguity can cause semantic inconsistency in sample pairs, degrading retrieval performance. Prior efforts either relied heavily on the quality of explicitly dividing clean and noisy subsets, or solely leveraged carefully selected single anchor information, neglecting relationships among diverse neighbors. In this paper, we propose a novel Graph-based Label Propagation (GLP) framework that learns pseudo-labels via label propagation on a sparse graph, enabling self-correction of noisy labels. Specifically, each modality's instances are treated as nodes, connected via k-nearest neighbor (kNN) search to form a sparse graph. Pseudo-label vectors are generated for all nodes within one modality to capture the matching degree of inter-modal nodes. Through iterative label propagation, the stabilized pseudo-labels implicitly exploit both intra- and inter-modal relationships to derive a reliable matching degree. A dynamic queue further enhances graph quality by updating high-quality nodes. Experiments on Flickr30K, MSCOCO, and CC120K show that our method outperforms state-of-the-art approaches, especially under high noise. Code is available at https://github.com/njustkmg/MM25-GLP.