  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.
