Siamese Feature Decoupling and Adaptive Prototype Alignment for Clothes Changed Person Re-Identification

Yuxuan Xiao, Jian Yang, Shanshan Zhang

Abstract: The core to tackle clothes changed person re-identification is to extract clothes-irrelevant features. Previous methods typically decoupled the entire image features into clothes-relevant and ID-relevant ones via an auxiliary task of clothes classification, yet in this way, it is not guaranteed that the ID-relevant features fully get rid of clothes-relevant information, as the entire image feature also includes other information, such as posture. To enhance the purity of ID-relevant features, this letter proposes a simple yet effective siamese feature decoupling and adaptive prototype alignment framework (Decoupling and Alignment, D${\&}$A), which involves a reference branch and an alignment branch. Specifically, the reference branch first uses human parsing to remove clothes information at the image level to ensure that the ID prototype subsequently extracted by the image encoder is purely ID-relevant without clothes; the alignment branch excludes clothes information from the ID-relevant features by dual-token decoupling, and then aligns the ID-relevant features with the ID prototypes in the reference branch to enhance the purity of the identity information obtained after decoupling. Furthermore, we introduce an adaptive update strategy to update the ID prototypes to prevent the ID prototypes and the decoupled features from shifting in feature space. Extensive experiments on widely used benchmarks (PRCC, LTCC, and VC-Clothes) validate the effectiveness of our method, which achieves new state-of-the-art results across most metrics.

External IDs:doi:10.1109/lsp.2025.3638256