Fengxia Han

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