  Existing works typically treat cross-domain semantic segmentation (CDSS) as a
data distribution mismatch problem and focus on aligning the marginal
distribution or conditional distribution. However, the label shift issue is
unfortunately overlooked, which actually commonly exists in the CDSS task, and
often causes a classifier bias in the learnt model. In this paper, we give an
in-depth analysis and show that the damage of label shift can be overcome by
aligning the data conditional distribution and correcting the posterior
probability. To this end, we propose a novel approach to undo the damage of the
label shift problem in CDSS. In implementation, we adopt class-level feature
alignment for conditional distribution alignment, as well as two simple yet
effective methods to rectify the classifier bias from source to target by
remolding the classifier predictions. We conduct extensive experiments on the
benchmark datasets of urban scenes, including GTA5 to Cityscapes and SYNTHIA to
Cityscapes, where our proposed approach outperforms previous methods by a large
margin. For instance, our model equipped with a self-training strategy reaches
59.3% mIoU on GTA5 to Cityscapes, pushing to a new state-of-the-art. The code
will be available at https://github.com/manmanjun/Undoing UDA.
