  Person Re-Identification (Re-ID) task seeks to enhance the tracking of
multiple individuals by surveillance cameras. It supports multimodal tasks,
including text-based person retrieval and human matching. One of the most
significant challenges faced in Re-ID is clothes-changing, where the same
person may appear in different outfits. While previous methods have made
notable progress in maintaining clothing data consistency and handling clothing
change data, they still rely excessively on clothing information, which can
limit performance due to the dynamic nature of human appearances. To mitigate
this challenge, we propose the Pose-Guidance Deep Supervision (PGDS), an
effective framework for learning pose guidance within the Re-ID task. It
consists of three modules: a human encoder, a pose encoder, and a Pose-to-Human
Projection module (PHP). Our framework guides the human encoder, i.e., the main
re-identification model, with pose information from the pose encoder through
multiple layers via the knowledge transfer mechanism from the PHP module,
helping the human encoder learn body parts information without increasing
computation resources in the inference stage. Through extensive experiments,
our method surpasses the performance of current state-of-the-art methods,
demonstrating its robustness and effectiveness for real-world applications. Our
code is available at https://github.com/huyquoctrinh/PGDS.
