Pose-Guided Attention Learning for Cloth-Changing Person Re-Identification

The change in appearance is a great challenge for cloth-changing person re-identification. Existing methods tackle this challenge by learning the shape features of the human body, however, these features are easily affected by the human pose or camera perspective. Thus, the ability to learn and extract the invariant features of person in varying conditions is crucial to overcome the above challenge. To address the issue of invariant feature extraction for cloth-changing person re-identification, a Pose-Guided Attention Learning (PGAL) framework is proposed in this paper. First, we introduce the human pose estimation network to remove the background effects and align the fine-grained key points features of human body. Then, to fully exploit the available appearance information, we develop a Feature Enhancement Module (FEM) that improves the feature representation of non-key point regions of human body through the Multi-Head Self Attention. Finally, in order to adaptively learn the invariant features of the person, we construct an Attention Learning Module (ALM) to achieve automatic selection of multi-granularity features by utilizing three different loss functions. Comparing with current popular methods on four cloth-changing person Re-ID datasets, the experimental results show the superiority of our method.