In this paper, we tackle the unsupervised domain adaptation (UDA) for semantic segmentation, which aims to segment the unlabeled real data using labeled synthetic data. The main problem of UDA for semantic segmentation relies on reducing the domain gap between the real image and synthetic image. To solve this problem, we focused on separating information in an image into content and style. Here, only the content has cues for semantic segmentation, and the style makes the domain gap. Thus, precise separation of content and style in an image leads to effect as supervision of real data even when learning with synthetic data. To make the best of this effect, we propose a zero-style loss. Even though we perfectly extract content for semantic segmentation in the real domain, another main challenge, the class imbalance problem, still exists in UDA for semantic segmentation. We address this problem by transferring the contents of tail classes from synthetic to real domain. Experimental results show that the proposed method achieves the state-of-the-art performance in semantic segmentation on the major two UDA settings.

翻译：在本文中,我们处理未经监督的语义分割域适应(UDA),目的是使用标签合成数据分割未标记的真实数据。 UDA 语义分割的主要问题取决于缩小真实图像和合成图像之间的域间差距。为了解决这个问题,我们侧重于将图像中的信息分离成内容和风格。在这里,只有内容可以提示语义分割,而风格可以造成域间差距。因此,在图像中精确地区分内容和风格,导致对真实数据的监督,即便在学习合成数据时也是如此。为了最佳地利用这一效果,我们提出了一种零式损失。尽管我们完全提取了真实域的语义分割内容,但另一个主要挑战是,类不平衡问题仍然存在于 UDA,用于语义分割。我们通过将尾尾类的内容从合成转移到真实域来解决这个问题。实验结果显示,拟议的方法在主要两个 UDA 设置的语义分割环境中实现了语义分割状态。