  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.
