DAST: Unsupervised Domain Adaptation in Semantic Segmentation Based on Discriminator Attention and Self-Training
F. Richard Yu, Mo Zhang, Hexin Dong, Sheng Hu, Bin Dong, Li Zhang
Abstract
Unsupervised domain adaption has recently been used to reduce the domain shift, which would ultimately improve the performance of the semantic segmentation on unlabeled real-world 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 (domain-specific) features, and then guides the model to focus on the latter. The self-training strategy adaptively improves the decision boundary of the model for the 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 the proposed method on numerous benchmark datasets.