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