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，在监督设置下已取得显著进展。然而，在跨域（即域泛化）场景中，由于测试域不可见以及训练集与测试集之间存在域偏移，行人重识别任务面临挑战。为解决该问题，现有方法主要致力于学习域不变或鲁棒特征。本文观察到，在样本对空间中训练集与测试集之间的数据分布差异小于样本实例空间。基于此观察，我们提出可泛化度量网络（GMN），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），并在正负样本对特征上进行训练，随后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，我们开发了配对身份中心（PIC）损失，通过确保相同配对身份的样本对特征一致性来提升模型鉴别力。通过在多个基准数据集上的大量实验，我们验证了所提方法的有效性，并确认了GMN中各模块的价值。