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

Kunlun Xu; Haotong Cheng; Jiangmeng Li; Xu Zou; Jiahuan Zhou

cs.CVMar 20, 2026

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

Authors: Kunlun Xu, Haotong Cheng, Jiangmeng Li, Xu Zou, Jiahuan Zhou

Organizations: Wangxuan Institute of Computer Technology, Peking University, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China

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

Figures & tables

Figure 1: (a) Existing methods rely on global feature learning when exploiting VLM, suffering from misguided attention on background and insufficient utilization of the human semantics. (b) Our method explicitly models and transfers human attribution knowledge across domains, achieving more precise human semantic acquisition and continual attribute knowledge reinforcement.

Figure 2: Overview of our framework. Given the new data $D_{t}$ , two stages operate sequentially. (a) Multi-grain Text Attribute Disentanglement mechanism conducts local attribution extraction and a global attribute model based on pretrained VLMs. Then, these disentangled multi-grain attributes are utilized by (b) Inter-domain Cross-modal Attribute Reinforcement scheme, which conducts visual-text alignment $\mathcal{L}_{MAlign}$ to guide visual attribute extraction. Besides, the inter-domain attribute alignment $\mathcal{L}_{DAlign}$ is also performed to accumulate the continually learned knowledge.

Net.	Method	Pub.	Market		SYSU		LPW		MSMT17		CUHK03		Seen-Avg		Unseen-Avg
Net.	Method	Pub.	mAP	R@1	mAP	R@1	mAP	R@1	mAP	R@1	mAP	R@1	mAP	R@1	mAP	R@1
ResNet	LwF [ 24 ]	TPAMI 2017	44.4	68.7	66.6	69.8	25.0	36.0	6.8	19.6	52.9	53.6	39.1	49.5	50.9	43.8
	DKP [ 45 ]	CVPR 2024	60.0	80.3	84.1	85.9	46.0	57.9	17.7	38.5	41.0	41.4	49.8	60.8	57.5	50.7
	LSTKC [ 46 ]	AAAI 2024	57.0	78.6	82.9	84.9	47.2	58.4	18.4	41.1	42.3	43.7	49.6	61.3	57.8	50.2
	DKP++ [ 53 ]	TPAMI 2025	52.1	76.5	75.6	77.7	45.2	58.1	14.9	36.1	40.1	42.2	45.6	58.1	56.6	48.5
	LSTKC++ [ 41 ]	TPAMI 2025	67.5	83.9	86.3	88.2	52.1	61.9	19.4	40.3	47.0	47.8	54.5	64.4	62.9	55.6

Table 1: Comparison results of Seen-domain anti-forgetting and UnSeen-domain generalization on Training Order-1.

Net.	Method	Pub.	LPW		MSMT17		Market		SYSU		CUHK03		Seen-Avg		Unseen-Avg
Net.	Method	Pub.	mAP	R@1	mAP	R@1	mAP	R@1	mAP	R@1	mAP	R@1	mAP	R@1	mAP	R@1
ResNet	LwF [ 24 ]	TPAMI 2017	38.8	50.5	6.7	19.2	43.9	67.1	73.2	75.9	50.1	51.9	42.5	52.9	50.0	42.7
	DKP [ 45 ]	CVPR 2024	49.5	61.4	14.1	32.6	60.3	80.6	84.5	86.4	43.6	43.7	50.4	60.9	59.5	52.4
	LSTKC [ 46 ]	AAAI 2024	46.7	57.6	14.9	33.9	56.5	78.0	84.0	86.1	42.1	43.7	48.8	59.9	57.4	49.5
	DKP++ [ 53 ]	TPAMI 2025	52.2	64.0	18.0	41.0	63.7	82.5	81.3	82.9	49.7	51.8	53.0	64.4	61.0	54.0
	LSTKC++ [ 41 ]	TPAMI 2025	55.7	65.7	15.0	34.3	66.9	83.6	86.3	87.6	48.4	49.9	54.4	64.2	63.4	56.0

Table 2: Comparison results of Seen-domain anti-forgetting and UnSeen-domain generalization on Training Order-2.

Figure 3: Tendency on seen domain knowledge accumulation.

Figure 4: Tendency on unseen domain generalization.

Figure 5: Attention map comparison across different approaches.

Baseline	$\mathcal{L}_{MAlign}$	$\mathcal{L}_{DAlign}$	Seen-Avg		Unseen-Avg
Baseline	$\mathcal{L}_{MAlign}$	$\mathcal{L}_{DAlign}$	mAP	R@1	mAP	R@1
$\checkmark$			57.1	66.6	66.4	58.8
$\checkmark$	$\checkmark$		66.3	75.8	74.6	68.0
$\checkmark$	$\checkmark$	$\checkmark$	70.4	79.9	77.8	72.0

Table 3: Ablation on the proposed model components.

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Person re-identification matches the same individual across camera views, times, clothing, and sensing modalities despite changes in appearance.

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Prompt-Anchored Vision-Text Distillation for Lifelong Person Re-identification

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University of Electronic Science and Technology of China, China · Harbin Institute of Technology, Shenzhen, China · Peking University, China

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Lifelong person re-identification (LReID) aims to train a generalizable model with sequentially collected data. However, such models often suffer from semantic drift, limited adaptability, and catastrophic forgetting as new domains emerge. Existing exemplar-free approaches largely rely on visual-only distillation or parameter regularization, while overlooking the potential of auxiliary modalities, such as text, to preserve semantic stability and enable incremental plasticity. We observe that the frozen text encoder in pretrained vision-language models can serve as a stable semantic anchor across domains. To decouple the roles of vision and text, we propose Prompt-Anchored vision-text Distillation (PAD), an asymmetric vision-text framework for semantic alignment and cross-domain generalization. On the textual side, we distill prompts to preserve vision-text alignment under a fixed semantic space, acting as a global semantic reference rather than a dominant learning signal. On the visual side, an EMA-based teacher with an adaptive prompt pool enables domain-wise adaptation by allocating new slots while freezing past ones. Extensive experiments show that PAD substantially outperforms state-of-the-art methods across seen and unseen domains, achieving a strong balance between stability and plasticity. Project page is available at https://github.com/zu-zi/PAD.

Abstract

Person Re-Identification

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