PGS: Pose-Guided Supervision for Mitigating Clothes-Changing in Person Re-Identification
Person Re-Identification (Re-ID) task seeks to enhance the tracking of multiple individuals by surveillance cameras. It provides additional support for multimodal tasks, including text-based person retrieval and human matching. Among the significant challenges faced in Re-ID, one of the most prominent is dealing with 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 tend to 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-Guided Supervision (PGS), an effective framework for learning pose guidance within the Re-ID task. Our PGS consists of three modules: a human encoder, a pose encoder, and a Pose-to-Human Projection module (PHP). The pose encoder module utilizes a frozen pre-trained model while we fine-tune a pre-trained human-centric model for the human encoder module. Our PHP transfers pose knowledge from the pose encoder module to the human encoder module through multiple projectors. Our framework, following extensive experimentation on five benchmark datasets, consistently surpasses the performance of current state-of-the-art methods. Our code is available at https://github.com/huyquoctrinh/PGS.
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Market-1501
CUHK03
