Multi-clues Adaptive Learning for Cloth-Changing Person Re-Identification

Solving long-term Cloth-Changing Person Re-identification (CC-ReID) requires extracting features insensitive to clothing such as face, silhouette, gait and pose estimation. Most current work focuses on modeling from a single feature, but we observe that CC-ReID problems in open environments are often difficult to solve solely based on a single feature, for instance, sometimes, contour features may be advantageous for recognition, while at other times gait features may be more valuable. In our paper, we suggest a novel multi-clues guided Adaptive Learning Transformer (ALT) which can adaptively select the most readily identifiable features based on different scenarios. The method comprises two parts: a Multi-clues Guiding Module (MGM) and a Feature Selection Module (FSM). We utilize clothes-irrelevant features from multi-modality information as clues, integrating multiple features to extract robust representations invariant to clothing changes for CC-ReID through cross-attention and Mixture of Experts (MoEs). We utilized contour sketch and gait as clues, conducting experiments on the CC-ReID dataset. The experimental results show that our recommended approach prevails over all other SOTA methods, particularly showing significant improvement compared to using contour sketch and gait alone.