  Unsupervised domain adaptation (UDA) in semantic segmentation is a
fundamental yet promising task relieving the need for laborious annotation
works. However, the domain shifts/discrepancies problem in this task compromise
the final segmentation performance. Based on our observation, the main causes
of the domain shifts are differences in imaging conditions, called image-level
domain shifts, and differences in object category configurations called
category-level domain shifts. In this paper, we propose a novel UDA pipeline
that unifies image-level alignment and category-level feature distribution
regularization in a coarse-to-fine manner. Specifically, on the coarse side, we
propose a photometric alignment module that aligns an image in the source
domain with a reference image from the target domain using a set of image-level
operators; on the fine side, we propose a category-oriented triplet loss that
imposes a soft constraint to regularize category centers in the source domain
and a self-supervised consistency regularization method in the target domain.
Experimental results show that our proposed pipeline improves the
generalization capability of the final segmentation model and significantly
outperforms all previous state-of-the-arts.
