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<p>Extracting robust features in complex scenes remains challenging in person re-identification (ReID). Convolutional neural networks (CNNs) have achieved great success but struggle to capture global pedestrian information. While Transformers excel at modeling global dependencies, they often miss local details. Although CNN-Transformer fusion has been explored, existing methods often adopt cascaded architectures or simple concatenation, which lack continuous and deep interaction between the two branches, thereby limiting their ability to exploit complementary visual information. To address these limitations, we propose a hierarchical deep synergistic fusion (HDSF) framework of CNN and Transformer for person ReID. HDSF facilitates bidirectional complementary interaction and feature enhancement between the two branches across multiple hierarchical levels, allowing them to fully complement and benefit each other. Specifically, we design a Deep Synergistic Fusion Mechanism (DSFM), comprising Bidirectional Feature Calibration (BFC) and Bidirectional Synergistic Enhancement (BSE) modules. The BFC module performs structural alignment and refinement of features between the heterogeneous branches. The BSE module facilitates both cross-branch interaction and self-branch optimization. Its key submodules include Cross-branch Complementary Enhancement (CCE), which enables complementary feature learning between branches, and Self-branch Salience Enhancement (SSE), which emphasizes salient identity-related cues within branches to reduce the influence of background clutter. Furthermore, we propose a multi-granularity supervision strategy to provide effective supervisory signals across multiple scales and semantic levels. Extensive experiments on four large-scale benchmarks demonstrate that HDSF outperforms most state-of-the-art approaches. Compared to our baseline, HDSF improves mAP and Rank-1 by 11.1% and 5.0% on Market1501, and by 12.2% and 8.8% on DukeMTMC.</p>

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Hierarchical deep synergistic fusion of CNN and transformer for person re-identification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月22日

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  • Yaxiong Liu,
  • Jingsong Li,
  • Guanzhi Lyu,
  • Yue Yang,
  • Zhenbo Li,
  • Yunling Liu

摘要

Extracting robust features in complex scenes remains challenging in person re-identification (ReID). Convolutional neural networks (CNNs) have achieved great success but struggle to capture global pedestrian information. While Transformers excel at modeling global dependencies, they often miss local details. Although CNN-Transformer fusion has been explored, existing methods often adopt cascaded architectures or simple concatenation, which lack continuous and deep interaction between the two branches, thereby limiting their ability to exploit complementary visual information. To address these limitations, we propose a hierarchical deep synergistic fusion (HDSF) framework of CNN and Transformer for person ReID. HDSF facilitates bidirectional complementary interaction and feature enhancement between the two branches across multiple hierarchical levels, allowing them to fully complement and benefit each other. Specifically, we design a Deep Synergistic Fusion Mechanism (DSFM), comprising Bidirectional Feature Calibration (BFC) and Bidirectional Synergistic Enhancement (BSE) modules. The BFC module performs structural alignment and refinement of features between the heterogeneous branches. The BSE module facilitates both cross-branch interaction and self-branch optimization. Its key submodules include Cross-branch Complementary Enhancement (CCE), which enables complementary feature learning between branches, and Self-branch Salience Enhancement (SSE), which emphasizes salient identity-related cues within branches to reduce the influence of background clutter. Furthermore, we propose a multi-granularity supervision strategy to provide effective supervisory signals across multiple scales and semantic levels. Extensive experiments on four large-scale benchmarks demonstrate that HDSF outperforms most state-of-the-art approaches. Compared to our baseline, HDSF improves mAP and Rank-1 by 11.1% and 5.0% on Market1501, and by 12.2% and 8.8% on DukeMTMC.