Mapping and Matching Joint Learning for Unpaired Unsupervised Visible-Infrared Person Re-Identification

Unsupervised visible-infrared person re-identification (UVI-ReID) aims to associate visible and infrared images of the same identities without relying on manual annotations. Existing approaches typically rely on the unrealistic assumption that the identities between the two modalities are completely overlapped. To advance the development of UVI-ReID, recent studies begin to explore the unpaired UVI-ReID task and introduce feature mapping strategies. Although these studies achieve certain progress, they overlook two critical factors: the identity consistency of the mapped features and the effectiveness of inter-modality overlapped identities (i.e., real cross-modality positive pairs). To this end, we propose a mapping and matching joint learning method (MMJL). First, we introduce an attention-guided feature mapping module, which reconstructs and weights features based on cross-modality attention weights to generate mapped features while preserving intra-identity consistency. Then, we introduce a cross-modality positive matching module, which identifies real cross-modality positive clusters based on the consistency between two cross-modality matching results and the similarity between the intra-modality mapped features and the target features. Moreover, we introduce multiple contrastive losses to simultaneously ensure the identity discriminability and modality invariance of person features. Extensive experiments on two unpaired visible-infrared datasets demonstrate the superiority of the proposed method and the effectiveness of each component.