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
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 Dt , 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 LMAlign to guide visual attribute extraction. Besides, the inter-domain attribute alignment LDAlign is also performed to accumulate the continually learned knowledge.
Net.
Method
Pub.
Market
SYSU
LPW
MSMT17
CUHK03
Seen-Avg
Unseen-Avg
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
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
LMAlign
LDAlign
Seen-Avg
Unseen-Avg
mAP
R@1
mAP
R@1
✓
57.1
66.6
66.4
58.8
✓
✓
66.3
75.8
74.6
68.0
✓
✓
✓
70.4
79.9
77.8
72.0
Table 3: Ablation on the proposed model components.
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.
Wen Wen, Hao Chen, Shiliang Zhang
University of Electronic Science and Technology of China, China · Harbin Institute of Technology, Shenzhen, China · Peking University, China
Recent vision-language models such as CLIP provide strong cross-modal alignment, but current CLIP-guided ReID pipelines rely on global features and fixed prompts. This limits their ability to capture fine-grained attribute cues and adapt to diverse appearances. We propose ALADIN, an attribute-language distillation network that distills knowledge from a frozen CLIP teacher to a lightweight ReID student. ALADIN introduces fine-grained attribute-local alignment to establish adaptive text-visual correspondence and robust representation learning. A Scene-Aware Prompt Generator produces image-specific soft prompts to facilitate adaptive alignment. Attribute-local distillation enforces consistency between textual attributes and local visual features, significantly enhancing robustness under occlusions. Furthermore, we employ cross-modal contrastive and relation distillation to preserve the inherent structural relationships among attributes. To provide precise supervision, we leverage Multimodal LLMs to generate structured attribute descriptions, which are then converted into localized attention maps via CLIP. At inference, only the student is used. Experiments on Market-1501, DukeMTMC-reID, and MSMT17 show improvements over CNN-, Transformer-, and CLIP-based methods, with better generalization and interpretability.
Wang Zhou, Boran Duan, Haojun Ai +2
Wuhan University No.299 Bayi Road, 430072, PR China
Domain Generalized person Re-identification (DG Re-ID) is a challenging task, where models are trained on source domains but tested on unseen target domains. Although previous pure vision-based models have achieved significant progress, the performance remains further improved. Recently, Vision-Language Models (VLMs) present outstanding generalization capabilities in various visual applications. However, directly adapting a VLM to Re-ID shows limited generalization improvement. This is because the VLM only produces with global features that are insensitive to ID nuances. To tacle this problem, we propose a CLIP-based multi-grained vision-language alignment framework in this work. Specifically, several multi-grained prompts are introduced in language modality to describe different body parts and align with their counterparts in vision modality. To obtain fine-grained visual information, an adaptively masked multi-head self-attention module is employed to precisely extract specific part features. To train the proposed module, an MLLM-based visual grounding expert is employed to automatically generate pseudo labels of body parts for supervision. Extensive experiments conducted on both single- and multi-source generalization protocols demonstrate the superior performance of our approach. The implementation code will be released at https://github.com/RikoLi/MUVA.
Jiachen Li, Xiaojin Gong, Dongping Zhang
College of Information Science and Electronic Engineering, Zhejiang University · College of Information Engineering, China Jiliang University