  Semantic segmentation models struggle to generalize in the presence of domain
shift. In this paper, we introduce contrastive learning for feature alignment
in cross-domain adaptation. We assemble both in-domain contrastive pairs and
cross-domain contrastive pairs to learn discriminative features that align
across domains. Based on the resulting well-aligned feature representations we
introduce a label expansion approach that is able to discover samples from hard
classes during the adaptation process to further boost performance. The
proposed approach consistently outperforms state-of-the-art methods for domain
adaptation. It achieves 60.2% mIoU on the Cityscapes dataset when training on
the synthetic GTA5 dataset together with unlabeled Cityscapes images.
