Boosting Noisy Correspondence Discrimination via Dynamic Neighborhood Semantic Verification

Noisy correspondence, characterized by mismatches in cross-modal data pairs, presents a significant challenge for real-world applications. Current approaches primarily rely on direct cross-modal pairwise similarity metrics, which suffer from two critical limitations: noise sensitivity, where direct similarity calculations are easily corrupted by noisy or ambiguous instances, and contextual blindness, where isolated pairwise comparisons fail to exploit the rich semantic context embedded in neighboring instances. To address this issue, we propose to improve noise correspondence discrimination through a well-designed Dynamic Neighborhood Semantic association verification paradigm, namely DNS. Specifically, we hypothesize that the matching degree of current samples can be quantified through the interrelationships among their respective semantic neighbors. For this reason, we develop a novel semantic drift distance and local relation proximity based on dynamic neighborhood association. Furthermore, beyond implicit approaches to semantic gap modeling in cross-modal data, we introduce an explicit decomposition framework that disentangles the gap into the semantic orientation and scalar magnitude. Through the strategic integration of these proposed mechanisms, DNS achieves substantial enhancement in noisy correspondence discrimination, yielding remarkable performance gains. Extensive experiments on three widely-used benchmark datasets, including Flickr30K, MS-COCO, and Conceptual Captions, demonstrate the superiority of DNS over state-of-the-art methods.