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 this https URL
Abstract (translated)
终身行人重识别（LReID）旨在从变化域中学习以获得统一的行人检索模型。现有LReID方法通常聚焦于从零开始或基于视觉分类预训练模型进行学习，而视觉语言模型（VLM）已在多种任务中展现出泛化知识。尽管现有方法可直接适配VLM，但由于它们仅考虑全局感知学习，细粒度属性知识未得到充分利用，导致知识获取与抗遗忘能力受限。针对此问题，我们提出一种新型VLM驱动的LReID方法，名为视觉语言属性解耦与增强（VLADR）。核心思想是显式建模人类通用共享属性以提升跨域知识迁移，从而有效利用历史知识强化新知识学习并缓解遗忘。具体而言，VLADR包含多粒度文本属性解耦机制，用于挖掘图像的全局与多样化局部文本属性。进而设计跨域跨模态属性增强方案，通过引入跨模态属性对齐引导视觉属性提取，并采用跨域属性对齐实现细粒度知识迁移。实验结果表明，我们的VLADR在抗遗忘与泛化能力上较最优方法提升1.9%-2.2%与2.1%-2.5%。源代码已公开于此https链接。
URL
https://arxiv.org/abs/2603.19678
