  Unsupervised domain adaptation (UDA) aims to adapt existing models of the
source domain to a new target domain with only unlabeled data. Most existing
methods suffer from noticeable negative transfer resulting from either the
error-prone discriminator network or the unreasonable teacher model. Besides,
the local regional consistency in UDA has been largely neglected, and only
extracting the global-level pattern information is not powerful enough for
feature alignment due to the abuse use of contexts. To this end, we propose an
uncertainty-aware consistency regularization method for cross-domain semantic
segmentation. Firstly, we introduce an uncertainty-guided consistency loss with
a dynamic weighting scheme by exploiting the latent uncertainty information of
the target samples. As such, more meaningful and reliable knowledge from the
teacher model can be transferred to the student model. We further reveal the
reason why the current consistency regularization is often unstable in
minimizing the domain discrepancy. Besides, we design a ClassDrop mask
generation algorithm to produce strong class-wise perturbations. Guided by this
mask, we propose a ClassOut strategy to realize effective regional consistency
in a fine-grained manner. Experiments demonstrate that our method outperforms
the state-of-the-art methods on four domain adaptation benchmarks, i.e., GTAV
$\rightarrow $ Cityscapes and SYNTHIA $\rightarrow $ Cityscapes, Virtual KITTI
$\rightarrow$ KITTI and Cityscapes $\rightarrow$ KITTI.
