Correspondence-Representation Co-Evolution for Weakly Supervised Visible-Infrared Person Re-Identification

Weakly supervised visible-infrared person re-identification learns from independently labeled visible and infrared images of the same training identities, without access to their cross-modal correspondences. When provisional matches supervise representation learning, early matching errors can distort the representation and be reinforced in subsequent correspondence inference. In this paper, we propose ReCUE, a correspondence-representation co-evolution framework that uses agreement among competing global assignments to guide supervision while keeping every correspondence revisable. Specifically, Global Correspondence Hypothesis Inference (GCHI) enumerates the K lowest-cost feasible one-to-one assignments and measures each candidate match's support by its frequency across these assignments. Structural Uncertainty-Aware Learning (SUAL) translates this global support into differentiated supervision: highly supported matches receive hard targets, ambiguous matches retain soft targets over competing counterparts, and weakly supported matches receive no correspondence-derived positive supervision. As the representation evolves, Correspondence-Representation Co-Evolution (CRCE) periodically reconstructs the complete correspondence state and associated supervision, allowing previously accepted matches to be reassigned or downgraded. Experiments on SYSU-MM01 and LLCM validate the effectiveness of ReCUE, which achieves 73.0%/69.0% Rank-1/mAP on SYSU-MM01 All Search and 57.0%/60.5% on LLCM VIS→IR. The code will be available.