  Recent advances in person re-identification (ReID) obtain impressive accuracy
in the supervised and unsupervised learning settings. However, most of the
existing methods need to train a new model for a new domain by accessing data.
Due to public privacy, the new domain data are not always accessible, leading
to a limited applicability of these methods. In this paper, we study the
problem of multi-source domain generalization in ReID, which aims to learn a
model that can perform well on unseen domains with only several labeled source
domains. To address this problem, we propose the Memory-based Multi-Source
Meta-Learning (M$^3$L) framework to train a generalizable model for unseen
domains. Specifically, a meta-learning strategy is introduced to simulate the
train-test process of domain generalization for learning more generalizable
models. To overcome the unstable meta-optimization caused by the parametric
classifier, we propose a memory-based identification loss that is
non-parametric and harmonizes with meta-learning. We also present a meta batch
normalization layer (MetaBN) to diversify meta-test features, further
establishing the advantage of meta-learning. Experiments demonstrate that our
M$^3$L can effectively enhance the generalization ability of the model for
unseen domains and can outperform the state-of-the-art methods on four
large-scale ReID datasets.
