DAST: Unsupervised Domain Adaptation in Semantic Segmentation Based on Discriminator Attention and Self-Training

Unsupervised domain adaption has recently been used to reduce the domain shift, which would ultimately improve the performance of semantic segmentation on unlabeled realworld data. In this paper, we follow the trend to propose a novel method to reduce the domain shift using strategies of discriminator attention and self-training. The discriminator attention strategy contains a two-stage adversarial learning process, which explicitly distinguishes the well-aligned (domain-invariant) and poorly-aligned (domainspecific) features, and then guides the model to focus on the latter. The self-training strategy adaptively improves the decision boundary of the model for target domain, which implicitly facilitates the extraction of domain-invariant features. By combining the two strategies, we find a more effective way to reduce the domain shift. Extensive experiments demonstrate the effectiveness of our proposed method on numerous benchmark datasets. The code is available at https://github.com/yufei1900/DAST segmentation.