Dual-branch manifold information consistency for unsupervised visible–infrared person re-identification

Unsupervised visible–infrared person re-identification focuses on the challenging task of matching individuals across different spectral modalities without labeled data. However, most existing pipelines construct correspondences exclusively from global representations, making them susceptible to modality-induced distortions that compromise cross-modal identity consistency. Moreover, the prevailing focus on label association often neglects the role of feature organization in preserving intra-class cohesion and inter-class separation, leading to identity dispersion and the erroneous grouping of visually similar but unrelated individuals. To address these limitations, we propose the dual-branch manifold information consistency framework comprising two modules. The first, dual-branch interactive feature enrichment, captures complementary global and region-specific patterns by building graph-based associations among image parts and applying attention-driven global–local interaction. The second, consistency-driven manifold refinement, learns modality-aware neighborhood structures via enhanced neighbor membership matrices and refines the manifold geometry through a globally aware coding rate-based objective and a locally aware cycle consistency constraint. Extensive experiments on popular datasets validate the superiority of our approach, highlighting its potential to significantly advance unsupervised visible–infrared person re-identification.