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

TL;DR

This paper introduces VLADR, a novel approach leveraging vision-language models to improve lifelong person re-identification by disentangling and reinforcing human attributes across domains, enhancing knowledge transfer and reducing forgetting.

Contribution

The paper proposes a new VLM-driven method with multi-grain attribute disentanglement and cross-modal reinforcement for improved lifelong person re-identification.

Findings

Outperforms state-of-the-art in anti-forgetting and generalization.

Effectively models global and local human attributes.

Enhances inter-domain knowledge transfer.

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