Graph-Enhanced Attribute-Aware Modeling for Cloth-Changing Person Re-Identification

Cloth-changing person re-identification aims to retrieve images of the same person across cameras when clothing may vary over time. Unlike conventional Re-ID, appearance cues such as clothing color and texture become highly unreliable in cloth-changing scenarios. Existing methods either rely on auxiliary clothing-invariant biometric cues that may introduce estimation noise, or model attribute semantics as isolated labels, making it difficult to simultaneously achieve robustness to attribute noise and fine-grained visual--semantic interaction. To address these issues, we propose a Graph-Enhanced Attribute-Aware Modeling framework, termed GEAM. Specifically, we first preprocess the attribute representation to explicitly suppress clothing-related semantics. We then introduce an adaptive attribute graph to model the latent dependencies among identity-related attributes, thereby alleviating the interference caused by attribute prediction errors. Based on the refined attribute representation, we further map the enhanced attribute semantics into compact semantic tokens and inject them into the visual backbone in a hierarchical manner, enabling local visual features to adaptively absorb identity-relevant yet clothing-irrelevant discriminative cues. This design effectively improves robustness to cloth-changing scenarios while preserving the stability of pretrained visual representations as much as possible. Extensive experiments on multiple mainstream CC-ReID benchmarks demonstrate that GEAM consistently outperforms a variety of state-of-the-art methods under the challenging clothes-changing setting. The source code and pretrained weights will be publicly released on GitHub.