Cloth-changing person reidentification (ReID) is a newly emerging research topic that is aimed at addressing the issues of large feature variations due to cloth-changing and pedestrian view/pose changes. Although significant progress has been achieved by introducing extra information (e.g., human contour sketching information, human body keypoints, and 3D human information), cloth-changing person ReID is still challenging due to impressionable pedestrian representations. Moreover, human semantic information and pedestrian identity information are not fully explored. To solve these issues, we propose a novel identity-guided collaborative learning scheme (IGCL) for cloth-changing person ReID, where the human semantic is fully utilized and the identity is unchangeable to guide collaborative learning. First, we design a novel clothing attention degradation stream to reasonably reduce the interference caused by clothing information where clothing attention and mid-level collaborative learning are employed. Second, we propose a human semantic attention and body jigsaw stream to highlight the human semantic information and simulate different poses of the same identity. In this way, the extraction features not only focus on human semantic information that is unrelated to the background but also are suitable for pedestrian pose variations. Moreover, a pedestrian identity enhancement stream is further proposed to enhance the identity importance and extract more favorable identity robust features. Most importantly, all these streams are jointly explored in an end-to-end unified framework, and the identity is utilized to guide the optimization. Extensive experiments on five public clothing person ReID datasets demonstrate that the proposed IGCL significantly outperforms SOTA methods and that the extracted feature is more robust, discriminative, and clothing-irrelevant.

翻译：换装行人重识别（ReID）是一个新兴的研究课题，旨在解决由服装变化及行人视角/姿态变化导致的特征差异大等问题。尽管通过引入额外信息（如人体轮廓素描信息、人体关键点和3D人体信息）已取得显著进展，但由于行人特征易受影响，换装行人ReID仍具挑战性。此外，人体语义信息与行人身份信息尚未被充分探索。为解决这些问题，我们提出一种新颖的身份引导协同学习方案（IGCL）用于换装行人ReID，该方案充分利用人体语义信息，并以不可变的身份信息引导协同学习。首先，我们设计了一种新颖的服装注意力降质流，通过融合服装注意力与中层协同学习，合理降低服装信息带来的干扰。其次，提出人体语义注意力与身体拼图流，用于突出人体语义信息并模拟同一身份的不同姿态。通过这种方式，提取的特征不仅聚焦于与背景无关的人体语义信息，还能适应行人姿态变化。此外，进一步提出行人身份增强流，以增强身份特征的重要性并提取更具鲁棒性的身份特征。最重要的是，所有上述分支均在一个端到端统一框架中联合探索，并以身份信息指导优化过程。在五个公开的服装行人ReID数据集上的大量实验表明，所提出的IGCL显著优于现有最优方法，提取的特征更具鲁棒性、判别性且与服装无关。