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<p>Person re-identification (re-ID) under domain shift remains brittle when conditions vary in viewpoint, illumination, and scene context. A domain-generalized (DG) feature learning framework is proposed that couples training-time domain-aware regularization with inference-time neighborhood calibration. During training, a refined neuron dropout (RND) extends domain-guided dropout via per-domain neuron-impact scores and temperature-controlled retention, suppressing domain-irrelevant activations while preserving domain-salient ones. At inference, a recursive reciprocal-expansion re-ranking (RRE) enforces reciprocal-neighborhood consistency to stabilize similarity estimates on large galleries. The components are architecture-neutral: RND operates on intermediate activations and RRE on distance matrices. The results are reported using a single compact CNN encoder to maintain a fixed computing budget and a fair comparison with lightweight baselines. Comprehensive ablations show stepwise CMC Rank-1 gains from baseline to + DGD, +RND, and + RRE. Comparisons against recent DG-ReID methods indicate competitive performance under pronounced variability in viewpoint, illumination, and background, with small gaps on certain normalization-centric settings, while improving cross-domain robustness on heterogeneous benchmarks.</p>

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Domain-generalized person re-identification via refined neuron dropout and reciprocal-expansion re-ranking

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月22日

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  • Yuan-Kai Wang,
  • Tung-Ming Pan,
  • Tian-You Chen

摘要

Person re-identification (re-ID) under domain shift remains brittle when conditions vary in viewpoint, illumination, and scene context. A domain-generalized (DG) feature learning framework is proposed that couples training-time domain-aware regularization with inference-time neighborhood calibration. During training, a refined neuron dropout (RND) extends domain-guided dropout via per-domain neuron-impact scores and temperature-controlled retention, suppressing domain-irrelevant activations while preserving domain-salient ones. At inference, a recursive reciprocal-expansion re-ranking (RRE) enforces reciprocal-neighborhood consistency to stabilize similarity estimates on large galleries. The components are architecture-neutral: RND operates on intermediate activations and RRE on distance matrices. The results are reported using a single compact CNN encoder to maintain a fixed computing budget and a fair comparison with lightweight baselines. Comprehensive ablations show stepwise CMC Rank-1 gains from baseline to + DGD, +RND, and + RRE. Comparisons against recent DG-ReID methods indicate competitive performance under pronounced variability in viewpoint, illumination, and background, with small gaps on certain normalization-centric settings, while improving cross-domain robustness on heterogeneous benchmarks.