  Aiming at recognizing images of the same person across distinct camera views,
person re-identification (re-ID) has been among active research topics in
computer vision. Most existing re-ID works require collection of a large amount
of labeled image data from the scenes of interest. When the data to be
recognized are different from the source-domain training ones, a number of
domain adaptation approaches have been proposed. Nevertheless, one still needs
to collect labeled or unlabelled target-domain data during training. In this
paper, we tackle an even more challenging and practical setting, domain
generalized (DG) person re-ID. That is, while a number of labeled source-domain
datasets are available, we do not have access to any target-domain training
data. In order to learn domain-invariant features without knowing the target
domain of interest, we present an episodic learning scheme which advances meta
learning strategies to exploit the observed source-domain labeled data. The
learned features would exhibit sufficient domain-invariant properties while not
overfitting the source-domain data or ID labels. Our experiments on four
benchmark datasets confirm the superiority of our method over the
state-of-the-arts.
