CMAG: Cross-Modal Attention and Graph-Enhanced Memory for Unsupervised Visible-Infrared Person Re-Identification

Unsupervised visible-infrared person re-identification (USL-VI-ReID) has garnered widespread attention due to its surveillance application value in complex environments. However, it faces four key challenges: modality discrepancy, batch training limitations, pseudo-label noise, and camera view bias. This paper proposes the CMAG (Cross-Modal Attention and Graph-enhanced Memory) framework, which innovatively combines circular topology structure with cross-modal attention mechanisms to address these challenges. CMAG introduces four core innovations: (1) applying circular topology structure to provide pseudo-label verification through detecting circular paths in feature space, effectively addressing the pseudo-label noise problem; (2) designing a cross-modal attention mechanism for Vision Transformers with residual fusion to balance modality-specific and shared information, solving the modality discrepancy issue; (3) constructing a graph-structured memory enhancement module with adaptive graph construction and multi-layer feature propagation to overcome batch training limitations; and (4) integrating camera-specific clustering with circular structure constraints to reduce camera background bias. Extensive experiments on SYSU-MM01 and RegDB datasets demonstrate the effectiveness of CMAG, achieving approximately 3.5% improvement in Rank-1 accuracy and 2.8% in mAP on average compared to state-of-the-art methods, validating our approach’s advantages in addressing key challenges in unsupervised cross-modal person re-identification.Code is available at https://github.com/hurryup186/CMAG.