DRFormer: A Dual-Regularized Bidirectional Transformer for Person Re-identification

• URL: http://arxiv.org/abs/2602.01059v1
• Date: Sun, 01 Feb 2026 06:59:53 GMT
• Title: DRFormer: A Dual-Regularized Bidirectional Transformer for Person Re-identification
• Authors: Ying Shu, Pujian Zhan, Huiqi Yang, Hehe Fan, Youfang Lin, Kai Lv,
• Abstract summary: Fine-grained discriminative details and global semantic features can contribute to solving person re-identification challenges.<n>Vision foundation models excel at mining local textures, and vision-language models capture strong global semantic difference.<n>We propose a framework to synergize their strengths by a textbfDual-textbfRegularized Bidirectional textbfTransformer.
• Score: 37.61695934257133
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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