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Convolutional neural network-based approaches for semantic segmentation relyon supervision with pixel-level ground truth, but may not generalize well tounseen image domains. As the labeling process is tedious and labor intensive,developing algorithms that can adapt source ground truth labels to the targetdomain is of great interest. In this paper, we propose an adversarial learningmethod for domain adaptation in the context of semantic segmentation.Considering semantic segmentations as structured outputs that contain spatialsimilarities between the source and target domains, we adopt adversariallearning in the output space. To further enhance the adapted model, weconstruct a multi-level adversarial network to effectively perform output spacedomain adaptation at different feature levels. Extensive experiments andablation study are conducted under various domain adaptation settings,including synthetic-to-real and cross-city scenarios. We show that the proposedmethod performs favorably against the state-of-the-art methods in terms ofaccuracy and visual quality.

Learning to Adapt Structured Output Space for Semantic Segmentation