  Feature alignment between domains is one of the mainstream methods for
Unsupervised Domain Adaptation (UDA) semantic segmentation. Existing feature
alignment methods for semantic segmentation learn domain-invariant features by
adversarial training to reduce domain discrepancy, but they have two limits: 1)
associations among pixels are not maintained, 2) the classifier trained on the
source domain couldn't adapted well to the target. In this paper, we propose a
new UDA semantic segmentation approach based on domain closeness assumption to
alleviate the above problems. Specifically, a prototype clustering strategy is
applied to cluster pixels with the same semantic, which will better maintain
associations among target domain pixels during the feature alignment. After
clustering, to make the classifier more adaptive, a normalized cut loss based
on the affinity graph of the target domain is utilized, which will make the
decision boundary target-specific. Sufficient experiments conducted on GTA5
$\rightarrow$ Cityscapes and SYNTHIA $\rightarrow$ Cityscapes proved the
effectiveness of our method, which illustrated that our results achieved the
new state-of-the-art.
