  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.
