  Unsupervised domain adaptation (UDA) aims at adapting the model trained on a
labeled source-domain dataset to an unlabeled target-domain dataset. The task
of UDA on open-set person re-identification (re-ID) is even more challenging as
the identities (classes) do not have overlap between the two domains. One major
research direction was based on domain translation, which, however, has fallen
out of favor in recent years due to inferior performance compared to
pseudo-label-based methods. We argue that the domain translation has great
potential on exploiting the valuable source-domain data but existing methods
did not provide proper regularization on the translation process. Specifically,
previous methods only focus on maintaining the identities of the translated
images while ignoring the inter-sample relations during translation. To tackle
the challenges, we propose an end-to-end structured domain adaptation framework
with an online relation-consistency regularization term. During training, the
person feature encoder is optimized to model inter-sample relations on-the-fly
for supervising relation-consistency domain translation, which in turn,
improves the encoder with informative translated images. The encoder can be
further improved with pseudo labels, where the source-to-target translated
images with ground-truth identities and target-domain images with pseudo
identities are jointly used for training. In the experiments, our proposed
framework is shown to achieve state-of-the-art performance on multiple UDA
tasks of person re-ID. With the synthetic-to-real translated images from our
structured domain-translation network, we achieved second place in the Visual
Domain Adaptation Challenge (VisDA) in 2020.
