AI RESEARCH
Vision-Language Attribute Disentanglement and Reinforcement for Lifelong Person Re-Identification
arXiv CS.CV
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ArXi:2603.19678v1 Announce Type: new 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.