Semantic segmentation with dense pixel-wise annotation has achieved excellent performance thanks to deep learning. However, the generalization of semantic segmentation in the wild remains challenging. In this paper, we address the problem of unsupervised domain adaptation (UDA) in semantic segmentation. Motivated by the fact that source and target domain have invariant semantic structures, we propose to exploit such invariance across domains by leveraging co-occurring patterns between pairwise pixels in the output of structured semantic segmentation. This is different from most existing approaches that attempt to adapt domains based on individual pixel-wise information in image, feature, or output level. Specifically, we perform domain adaptation on the affinity relationship between adjacent pixels termed affinity space of source and target domain. To this end, we develop two affinity space adaptation strategies: affinity space cleaning and adversarial affinity space alignment. Extensive experiments demonstrate that the proposed method achieves superior performance against some state-of-the-art methods on several challenging benchmarks for semantic segmentation across domains. The code is available at https://github.com/idealwei/ASANet.

中文翻译：

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跨域语义分割的亲和空间适应


由于深度学习，具有密集像素级注释的语义分割取得了优异的性能。然而，语义分割的泛化仍然具有挑战性。在本文中，我们解决了语义分割中的无监督域适应（UDA）问题。由于源域和目标域具有不变的语义结构这一事实，我们建议通过利用结构化语义分割输出中成对像素之间的共现模式来利用跨域的这种不变性。这与大多数现有方法不同，大多数现有方法试图根据图像、特征或输出级别中的各个像素信息来调整域。具体来说，我们对相邻像素之间的亲和关系（称为源域和目标域的亲和空间）进行域自适应。为此，我们开发了两种亲和空间适应策略：亲和空间清理和对抗性亲和空间对齐。大量的实验表明，所提出的方法在跨领域语义分割的几个具有挑战性的基准上比一些最先进的方法取得了优越的性能。该代码可在 https://github.com/idealwei/ASANet 上获取。