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

Affinity Space Adaptation for Semantic Segmentation Across Domains