Domain generalization person re-identification (DG-ReID) aims to train a model on source domains and generalize well on unseen domains. Vision Transformer usually yields better generalization ability than common CNN networks under distribution shifts. However, Transformer-based ReID models inevitably over-fit to domain-specific biases due to the supervised learning strategy on the source domain. We observe that while the global images of different IDs should have different features, their similar local parts (e.g., black backpack) are not bounded by this constraint. Motivated by this, we propose a pure Transformer model (termed Part-aware Transformer) for DG-ReID by designing a proxy task, named Cross-ID Similarity Learning (CSL), to mine local visual information shared by different IDs. This proxy task allows the model to learn generic features because it only cares about the visual similarity of the parts regardless of the ID labels, thus alleviating the side effect of domain-specific biases. Based on the local similarity obtained in CSL, a Part-guided Self-Distillation (PSD) is proposed to further improve the generalization of global features. Our method achieves state-of-the-art performance under most DG ReID settings. Under the Market$\to$Duke setting, our method exceeds state-of-the-art by 10.9% and 12.8% in Rank1 and mAP, respectively. The code is available at https://github.com/liyuke65535/Part-Aware-Transformer.

翻译：领域泛化行人重识别（DG-ReID）旨在源域训练模型并在未见域上实现良好泛化。视觉Transformer在分布偏移下通常比传统CNN网络具有更强的泛化能力。然而，基于Transformer的重识别模型因在源域采用监督学习策略，不可避免地会过拟合于特定领域的偏差。我们观察到，虽然不同身份的整体图像应具有差异化特征，但其相似局部部位（如黑色背包）不受此约束。受此启发，我们提出一种纯Transformer模型（称为感知部位Transformer）用于DG-ReID，通过设计名为跨身份相似性学习（CSL）的代理任务来挖掘不同身份共享的局部视觉信息。该代理任务仅关注部位视觉相似性而与身份标签无关，使模型能够学习通用特征，从而缓解领域特定偏差的负面影响。基于CSL获得的局部相似性，我们进一步提出部位引导自蒸馏（PSD）来提升全局特征的泛化能力。在大多数DG ReID设置下，我们的方法均达到了最先进水平。在Market→Duke设置中，本方法在Rank1和mAP上分别以10.9%和12.8%的幅度超越现有最优方法。代码已开源至https://github.com/liyuke65535/Part-Aware-Transformer。