Vision-Language Attribute Disentanglement and Reinforcement for Lifelong Person Re-Identification

• URL: http://arxiv.org/abs/2603.19678v1
• Date: Fri, 20 Mar 2026 06:23:05 GMT
• Title: Vision-Language Attribute Disentanglement and Reinforcement for Lifelong Person Re-Identification
• Abstract summary: Lifelong person re-identification (LReID) aims to learn from varying domains to obtain a unified person retrieval model.<n>We introduce a novel VLM-driven LReID approach named Vision-Language Attribute Disentanglement and Reinforcement.<n>Our key idea is to explicitly model the universally shared human attributes to improve inter-domain knowledge transfer.
• Score: 39.00527450168451
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lifelong person re-identification (LReID) aims to learn from varying domains to obtain a unified person retrieval model. Existing LReID approaches typically focus on learning from scratch or a visual classification-pretrained model, while the Vision-Language Model (VLM) has shown generalizable knowledge in a variety of tasks. Although existing methods can be directly adapted to the VLM, since they only consider global-aware learning, the fine-grained attribute knowledge is underleveraged, leading to limited acquisition and anti-forgetting capacity. To address this problem, we introduce a novel VLM-driven LReID approach named Vision-Language Attribute Disentanglement and Reinforcement (VLADR). Our key idea is to explicitly model the universally shared human attributes to improve inter-domain knowledge transfer, thereby effectively utilizing historical knowledge to reinforce new knowledge learning and alleviate forgetting. Specifically, VLADR includes a Multi-grain Text Attribute Disentanglement mechanism that mines the global and diverse local text attributes of an image. Then, an Inter-domain Cross-modal Attribute Reinforcement scheme is developed, which introduces cross-modal attribute alignment to guide visual attribute extraction and adopts inter-domain attribute alignment to achieve fine-grained knowledge transfer. Experimental results demonstrate that our VLADR outperforms the state-of-the-art methods by 1.9\%-2.2\% and 2.1\%-2.5\% on anti-forgetting and generalization capacity. Our source code is available at https://github.com/zhoujiahuan1991/CVPR2026-VLADR

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