Person Re-identification (Re-ID) is a crucial technique for public security and has made significant progress in supervised settings. However, the cross-domain (i.e., domain generalization) scene presents a challenge in Re-ID tasks due to unseen test domains and domain-shift between the training and test sets. To tackle this challenge, most existing methods aim to learn domain-invariant or robust features for all domains. In this paper, we observe that the data-distribution gap between the training and test sets is smaller in the sample-pair space than in the sample-instance space. Based on this observation, we propose a Generalizable Metric Network (GMN) to further explore sample similarity in the sample-pair space. Specifically, we add a Metric Network (M-Net) after the main network and train it on positive and negative sample-pair features, which is then employed during the test stage. Additionally, we introduce the Dropout-based Perturbation (DP) module to enhance the generalization capability of the metric network by enriching the sample-pair diversity. Moreover, we develop a Pair-Identity Center (PIC) loss to enhance the model's discrimination by ensuring that sample-pair features with the same pair-identity are consistent. We validate the effectiveness of our proposed method through a lot of experiments on multiple benchmark datasets and confirm the value of each module in our GMN.

翻译：行人重识别（Re-ID）是公共安全领域的关键技术，并在监督场景下取得了显著进展。然而，跨域（即域泛化）场景因存在未见测试域以及训练集与测试集之间的域偏移，对行人重识别任务构成了挑战。为解决这一难题，现有方法大多致力于学习域不变性或鲁棒性特征。本文观察到，训练集与测试集间的数据分布差异在样本对空间中比在样本实例空间中更小。基于此观察，我们提出了一种通用度量网络（GMN），以进一步探索样本对空间中的样本相似性。具体而言，我们在主网络之后添加度量网络（M-Net），利用正负样本对特征对其进行训练，并在测试阶段使用该网络。此外，我们引入基于丢弃的扰动（DP）模块，通过丰富样本对多样性来增强度量网络的泛化能力。同时，我们设计了配对身份中心（PIC）损失，通过确保具有相同配对身份的样本对特征一致性来提升模型判别力。通过在多个基准数据集上进行大量实验，我们验证了所提方法的有效性，并确认了GMN中每个模块的价值。