  Domain generalization (DG) has attracted much attention in person
re-identification (ReID) recently. It aims to make a model trained on multiple
source domains generalize to an unseen target domain. Although achieving
promising progress, existing methods usually need the source domains to be
labeled, which could be a significant burden for practical ReID tasks. In this
paper, we turn to investigate unsupervised domain generalization for ReID, by
assuming that no label is available for any source domains.
  To address this challenging setting, we propose a simple and efficient
domain-specific adaptive framework, and realize it with an adaptive
normalization module designed upon the batch and instance normalization
techniques. In doing so, we successfully yield reliable pseudo-labels to
implement training and also enhance the domain generalization capability of the
model as required. In addition, we show that our framework can even be applied
to improve person ReID under the settings of supervised domain generalization
and unsupervised domain adaptation, demonstrating competitive performance with
respect to relevant methods. Extensive experimental study on benchmark datasets
is conducted to validate the proposed framework. A significance of our work
lies in that it shows the potential of unsupervised domain generalization for
person ReID and sets a strong baseline for the further research on this topic.
