Abstract
Person Re-Identification (ReID) faces severe challenges from modality discrepancy and clothing variation in long-term surveillance scenario. While existing studies have made significant progress in either Visible-Infrared ReID (VI-ReID) or Clothing-Change ReID (CC-ReID), real-world surveillance system often face both challenges simultaneously. To address this overlooked yet realistic problem, we define a new task, termed Cross-Modality Clothing-Change Re-Identification (CMCC-ReID), which targets pedestrian matching across variations in both modality and clothing. To advance research in this direction, we construct a new benchmark SYSU-CMCC, where each identity is captured in both visible and infrared domains with distinct outfits, reflecting the dual heterogeneity of long-term surveillance. To tackle CMCC-ReID, we propose a Progressive Identity Alignment Network (PIA) that progressively mitigates the issues of clothing variation and modality discrepancy. Specifically, a Dual-Branch Disentangling Learning (DBDL) module separates identity-related cues from clothing-related factors to achieve clothing-agnostic representation, and a Bi-Directional Prototype Learning (BPL) module performs intra-modality and inter-modality contrast in the embedding space to bridge the modality gap while further suppressing clothing interference. Extensive experiments on the SYSU-CMCC dataset demonstrate that PIA establishes a strong baseline for this new task and significantly outperforms existing methods.
Abstract (translated)
行人重识别（ReID）在长期监控场景中面临模态差异与衣着变化的双重严峻挑战。尽管现有研究在可见光-红外ReID（VI-ReID）或衣着变化ReID（CC-ReID）单一方面已取得显著进展，但实际监控系统往往需同时应对这两种问题。针对这一被忽视却极具现实意义的问题，我们定义了一项新任务——跨模态衣着变化重识别（CMCC-ReID），旨在实现跨模态与衣着变化双重变异下的行人匹配。为推进该方向研究，我们构建了新基准数据集SYSU-CMCC，其中每个身份均以不同衣着在可见光与红外双模态下采集，以反映长期监控中的双重异构性。为应对CMCC-ReID挑战，我们提出渐进式身份对齐网络（PIA），通过逐步缓解衣着变化与模态差异问题。具体而言，双分支解耦学习（DBDL）模块将身份相关特征与衣着相关因素分离，以获得与衣着无关的表征；双向原型学习（BPL）模块在嵌入空间中进行模态内与模态间对比，在弥合模态差距的同时进一步抑制衣着干扰。在SYSU-CMCC数据集上的大量实验表明，PIA为该新任务建立了强基线，并显著超越现有方法。
URL
https://arxiv.org/abs/2604.02808
