Decoupled Identity and Attribute Tokenization for Person Re-Identification

Vision-language models like CLIP have revolutionized person re-identification (ReID) by enabling cross-modal semantic alignment. However, most of the existing CLIP-based ReID methods suffer from a critical limitation: semantic entanglement, where identity and attribute features are indiscriminately compressed into a single, undifferentiated token representation. This oversight fails to account for their inherently distinct roles in characterizing individuals.To address this limitation, we propose an Identity-Attribute-Decoupled Tokenization (IADT) method, a hierarchical framework with two synergistic components:Subject-oriented tokens that model identity through a cross-modality feature inverse mapping paradigm, preserving invariant biometric features;Attribute-aware tokens that capture localized characteristics through the cross-interaction of local features and learnable prototype vectors, dynamically focusing on discriminative regions without manual supervision.The hierarchical tokenization enables disentangled yet complementary representation learning: Identity and attribute semantics are encoded into distinct embedding subspaces, while cross-token contrastive learning establishes semantic reinforcement through attention-guided feature interaction. Crucially, this process does not require part-level annotations, making it directly applicable to real-world deployment. Extensive experiments validate effectiveness of the proposed method. For example, on the Market-1501 dataset, IADT achieves 97.1% mAP (+2.5% over SOTA) and 98.2% Rank-1 accuracy. For the challenging MSMT benchmark, it attains 88.9% mAP (+1.7% improvement) with 93.1% Rank-1 accuracy, demonstrating consistent superiority. The code will be available at https://github.com/llraay/IADT.