Cloth-Changing Person Re-Identification (CC-ReID) aims to accurately identify the target person in more realistic surveillance scenarios, where pedestrians usually change their clothing. Despite great progress, limited cloth-changing training samples in existing CC-ReID datasets still prevent the model from adequately learning cloth-irrelevant features. In addition, due to the absence of explicit supervision to keep the model constantly focused on cloth-irrelevant areas, existing methods are still hampered by the disruption of clothing variations. To solve the above issues, we propose an Identity-aware Dual-constraint Network (IDNet) for the CC-ReID task. Specifically, to help the model extract cloth-irrelevant clues, we propose a Clothes Diversity Augmentation (CDA), which generates more realistic cloth-changing samples by enriching the clothing color while preserving the texture. In addition, a Multi-scale Constraint Block (MCB) is designed, which extracts fine-grained identity-related features and effectively transfers cloth-irrelevant knowledge. Moreover, a Counterfactual-guided Attention Module (CAM) is presented, which learns cloth-irrelevant features from channel and space dimensions and utilizes the counterfactual intervention for supervising the attention map to highlight identity-related regions. Finally, a Semantic Alignment Constraint (SAC) is designed to facilitate high-level semantic feature interaction. Comprehensive experiments on four CC-ReID datasets indicate that our method outperforms prior state-of-the-art approaches.

翻译：换装行人重识别（CC-ReID）旨在更真实的监控场景中准确识别目标行人，此时行人通常会更换服装。尽管已有较大进展，现有CC-ReID数据集中有限的换装训练样本仍阻碍了模型充分学习与衣物无关的特征。此外，由于缺乏显式监督使模型持续关注与衣物无关的区域，现有方法仍受衣物变化的干扰。为解决上述问题，我们提出一种面向CC-ReID任务的身份感知双约束网络（IDNet）。具体地，为帮助模型提取与衣物无关的线索，我们提出衣物多样性增强（CDA）方法，通过丰富衣物颜色同时保持纹理，生成更真实的换装样本。同时，设计多尺度约束模块（MCB），提取细粒度身份相关特征并有效传递与衣物无关的知识。此外，提出反事实引导注意力模块（CAM），从通道和空间维度学习与衣物无关的特征，并利用反事实干预监督注意力图以突出身份相关区域。最后，设计语义对齐约束（SAC）促进高层语义特征交互。在四个CC-ReID数据集上的全面实验表明，我们的方法优于现有最先进方法。