  Unsupervised domain adaptive (UDA) person re-identification (ReID) aims to
transfer the knowledge from the labeled source domain to the unlabeled target
domain for person matching. One challenge is how to generate target domain
samples with reliable labels for training. To address this problem, we propose
a Disentanglement-based Cross-Domain Feature Augmentation (DCDFA) strategy,
where the augmented features characterize well the target and source domain
data distributions while inheriting reliable identity labels. Particularly, we
disentangle each sample feature into a robust domain-invariant/shared feature
and a domain-specific feature, and perform cross-domain feature recomposition
to enhance the diversity of samples used in the training, with the constraints
of cross-domain ReID loss and domain classification loss. Each recomposed
feature, obtained based on the domain-invariant feature (which enables a
reliable inheritance of identity) and an enhancement from a domain specific
feature (which enables the approximation of real distributions), is thus an
"ideal" augmentation. Extensive experimental results demonstrate the
effectiveness of our method, which achieves the state-of-the-art performance.
