  We introduce a novel approach to unsupervised and semi-supervised domain
adaptation for semantic segmentation. Unlike many earlier methods that rely on
adversarial learning for feature alignment, we leverage contrastive learning to
bridge the domain gap by aligning the features of structurally similar label
patches across domains. As a result, the networks are easier to train and
deliver better performance. Our approach consistently outperforms
state-of-the-art unsupervised and semi-supervised methods on two challenging
domain adaptive segmentation tasks, particularly with a small number of target
domain annotations. It can also be naturally extended to weakly-supervised
domain adaptation, where only a minor drop in accuracy can save up to 75% of
annotation cost.
