  Domain generalizable (DG) person re-identification (ReID) is a challenging
problem because we cannot access any unseen target domain data during training.
Almost all the existing DG ReID methods follow the same pipeline where they use
a hybrid dataset from multiple source domains for training, and then directly
apply the trained model to the unseen target domains for testing. These methods
often neglect individual source domains' discriminative characteristics and
their relevances w.r.t. the unseen target domains, though both of which can be
leveraged to help the model's generalization. To handle the above two issues,
we propose a novel method called the relevance-aware mixture of experts
(RaMoE), using an effective voting-based mixture mechanism to dynamically
leverage source domains' diverse characteristics to improve the model's
generalization. Specifically, we propose a decorrelation loss to make the
source domain networks (experts) keep the diversity and discriminability of
individual domains' characteristics. Besides, we design a voting network to
adaptively integrate all the experts' features into the more generalizable
aggregated features with domain relevance. Considering the target domains'
invisibility during training, we propose a novel learning-to-learn algorithm
combined with our relation alignment loss to update the voting network.
Extensive experiments demonstrate that our proposed RaMoE outperforms the
state-of-the-art methods.
