Cloth-changing Person Re-Identification (CC-ReID) is a challenging task that aims to retrieve the target person across multiple surveillance cameras when clothing changes might happen. Despite recent progress in CC-ReID, existing approaches are still hindered by the interference of clothing variations since they lack effective constraints to keep the model consistently focused on clothing-irrelevant regions. To address this issue, we present a Semantic-aware Consistency Network (SCNet) to learn identity-related semantic features by proposing effective consistency constraints. Specifically, we generate the black-clothing image by erasing pixels in the clothing area, which explicitly mitigates the interference from clothing variations. In addition, to fully exploit the fine-grained identity information, a head-enhanced attention module is introduced, which learns soft attention maps by utilizing the proposed part-based matching loss to highlight head information. We further design a semantic consistency loss to facilitate the learning of high-level identity-related semantic features, forcing the model to focus on semantically consistent cloth-irrelevant regions. By using the consistency constraint, our model does not require any extra auxiliary segmentation module to generate the black-clothing image or locate the head region during the inference stage. Extensive experiments on four cloth-changing person Re-ID datasets (LTCC, PRCC, Vc-Clothes, and DeepChange) demonstrate that our proposed SCNet makes significant improvements over prior state-of-the-art approaches. Our code is available at: https://github.com/Gpn-star/SCNet.

翻译：换装行人重识别（CC-ReID）是一项具有挑战性的任务，旨在当服装可能发生变化时，跨多个监控摄像头检索目标行人。尽管近期在CC-ReID方面取得了进展，现有方法仍受到服装变化干扰的阻碍，因为它们缺乏有效约束来使模型持续关注与服装无关的区域。为了解决这一问题，我们提出语义感知一致性网络（SCNet），通过引入有效的一致性约束学习与身份相关的语义特征。具体而言，我们通过擦除服装区域的像素生成黑色服装图像，从而明确减轻服装变化的干扰。此外，为充分挖掘细粒度身份信息，我们引入头部增强注意力模块，利用所提出的基于部件的匹配损失学习软注意力图以突出头部信息。我们进一步设计语义一致性损失以促进高层身份相关语义特征的学习，迫使模型聚焦于语义一致的非服装区域。通过使用这一一致性约束，我们的模型在推理阶段无需任何额外的辅助分割模块来生成黑色服装图像或定位头部区域。在四个换装行人重识别数据集（LTCC、PRCC、Vc-Clothes和DeepChange）上的大量实验表明，我们提出的SCNet相比先前最先进的方法取得了显著改进。我们的代码开源于：https://github.com/Gpn-star/SCNet。