Semantic Segmentation via Domain Adaptation with Global Structure Embedding

In this paper we focus on the problem of unsupervised domain adaptation for semantic segmentation. The previous works usually focus on adversarial learning either in pixel-level or feature-level. However, global structure knowledge is often neglected in the adversarial learning due to the possible reasons: First, the result of pixel-level adversarial learning does not necessarily preserve the semantic consistency of the input image. Second, global structure knowledge is not embedded to regularize the feature-level adversarial learning. In this work, we propose a framework for unsupervised domain adaptation in semantic segmentation which effectively incorporates pixel- level, feature-level adversarial learning and self-training strategy. Our framework embeds the global structure knowledge into the adversarial training step to tackle the problem of structure misalignment. Consequently, our proposed framework achieves the state-of-the-art semantic segmentation domain adaptation results on the task of transferring GTA5 to Cityscapes.