  We consider the problem of unsupervised domain adaptation for semantic
segmentation by easing the domain shift between the source domain (synthetic
data) and the target domain (real data) in this work. State-of-the-art
approaches prove that performing semantic-level alignment is helpful in
tackling the domain shift issue. Based on the observation that stuff categories
usually share similar appearances across images of different domains while
things (i.e. object instances) have much larger differences, we propose to
improve the semantic-level alignment with different strategies for stuff
regions and for things: 1) for the stuff categories, we generate feature
representation for each class and conduct the alignment operation from the
target domain to the source domain; 2) for the thing categories, we generate
feature representation for each individual instance and encourage the instance
in the target domain to align with the most similar one in the source domain.
In this way, the individual differences within thing categories will also be
considered to alleviate over-alignment. In addition to our proposed method, we
further reveal the reason why the current adversarial loss is often unstable in
minimizing the distribution discrepancy and show that our method can help ease
this issue by minimizing the most similar stuff and instance features between
the source and the target domains. We conduct extensive experiments in two
unsupervised domain adaptation tasks, i.e. GTA5 to Cityscapes and SYNTHIA to
Cityscapes, and achieve the new state-of-the-art segmentation accuracy.
