  Unsupervised domain adaptation for semantic segmentation aims to make models
trained on synthetic data (source domain) adapt to real images (target domain).
Previous feature-level adversarial learning methods only consider adapting
models on the high-level semantic features. However, the large domain gap
between source and target domains in the high-level semantic features makes
accurate adaptation difficult. In this paper, we present the first attempt at
explicitly using low-level edge information, which has a small inter-domain
gap, to guide the transfer of semantic information. To this end, a
semantic-edge domain adaptation architecture is proposed, which uses an
independent edge stream to process edge information, thereby generating
high-quality semantic boundaries over the target domain. Then, an edge
consistency loss is presented to align target semantic predictions with
produced semantic boundaries. Moreover, we further propose two entropy
reweighting methods for semantic adversarial learning and self-supervised
learning, respectively, which can further enhance the adaptation performance of
our architecture. Comprehensive experiments on two UDA benchmark datasets
demonstrate the superiority of our architecture compared with state-of-the-art
methods.
