CMCC-ReID: Cross-Modality Clothing-Change Person Re-Identification

Person Re-Identification (ReID) faces severe challenges frommodality discrepancy and clothing variation in long-term surveillancescenario. While existing studies have made significant progress in ei-ther Visible-Infrared ReID (VI-ReID) or Clothing-Change ReID (CC-ReID), real-world surveillance system often face both challenges simul-taneously. To address this overlooked yet realistic problem, we definea new task, termed Cross-Modality Clothing-Change Re-Identification(CMCC-ReID), which targets pedestrian matching across variations inboth modality and clothing. To advance research in this direction, weconstruct a new benchmark SYSU-CMCC, where each identity is cap-tured in both visible and infrared domains with distinct outfits, reflectingthe dual heterogeneity of long-term surveillance. To tackle CMCC-ReID,we propose a Progressive Identity Alignment Network (PIA) that pro-gressively mitigates the issues of clothing variation and modality dis-crepancy. Specifically, a Dual-Branch Disentangling Learning (DBDL)module separates identity-related cues from clothing-related factors toachieve clothing-agnostic representation, and a Bi-Directional PrototypeLearning (BPL) module performs intra-modality and inter-modality con-trast in the embedding space to bridge the modality gap while furthersuppressing clothing interference. Extensive experiments on the SYSU-CMCC dataset demonstrate that PIA establishes a strong baseline forthis new task and significantly outperforms existing methods.