DRFormer: A Dual-Regularized Bidirectional Transformer for Person Re-identification
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866912866771140608 |
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| author | Shu, Ying Zhan, Pujian Yang, Huiqi Fan, Hehe Lin, Youfang Lv, Kai |
| author_facet | Shu, Ying Zhan, Pujian Yang, Huiqi Fan, Hehe Lin, Youfang Lv, Kai |
| contents | Both fine-grained discriminative details and global semantic features can contribute to solving person re-identification challenges, such as occlusion and pose variations. Vision foundation models (\textit{e.g.}, DINO) excel at mining local textures, and vision-language models (\textit{e.g.}, CLIP) capture strong global semantic difference. Existing methods predominantly rely on a single paradigm, neglecting the potential benefits of their integration. In this paper, we analyze the complementary roles of these two architectures and propose a framework to synergize their strengths by a \textbf{D}ual-\textbf{R}egularized Bidirectional \textbf{Transformer} (\textbf{DRFormer}). The dual-regularization mechanism ensures diverse feature extraction and achieves a better balance in the contributions of the two models. Extensive experiments on five benchmarks show that our method effectively harmonizes local and global representations, achieving competitive performance against state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01059 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DRFormer: A Dual-Regularized Bidirectional Transformer for Person Re-identification Shu, Ying Zhan, Pujian Yang, Huiqi Fan, Hehe Lin, Youfang Lv, Kai Computer Vision and Pattern Recognition Multimedia Both fine-grained discriminative details and global semantic features can contribute to solving person re-identification challenges, such as occlusion and pose variations. Vision foundation models (\textit{e.g.}, DINO) excel at mining local textures, and vision-language models (\textit{e.g.}, CLIP) capture strong global semantic difference. Existing methods predominantly rely on a single paradigm, neglecting the potential benefits of their integration. In this paper, we analyze the complementary roles of these two architectures and propose a framework to synergize their strengths by a \textbf{D}ual-\textbf{R}egularized Bidirectional \textbf{Transformer} (\textbf{DRFormer}). The dual-regularization mechanism ensures diverse feature extraction and achieves a better balance in the contributions of the two models. Extensive experiments on five benchmarks show that our method effectively harmonizes local and global representations, achieving competitive performance against state-of-the-art methods. |
| title | DRFormer: A Dual-Regularized Bidirectional Transformer for Person Re-identification |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2602.01059 |