  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.

Computer Vision and Pattern Recognition