  "Self-training" has become a dominant method for semantic segmentation via
unsupervised domain adaptation (UDA). It creates a set of pseudo labels for the
target domain to give explicit supervision. However, the pseudo labels are
noisy, sparse and do not provide any information about inter-pixel
correlations. We regard inter-pixel correlation quite important because
semantic segmentation is a task of predicting highly structured pixel-level
outputs. Therefore, in this paper, we propose a method of transferring the
inter-pixel correlations from the source domain to the target domain via a
self-attention module. The module takes the prediction of the segmentation
network as an input and creates a self-attended prediction that correlates
similar pixels. The module is trained only on the source domain to learn the
domain-invariant inter-pixel correlations, then later, it is used to train the
segmentation network on the target domain. The network learns not only from the
pseudo labels but also by following the output of the self-attention module
which provides additional knowledge about the inter-pixel correlations. Through
extensive experiments, we show that our method significantly improves the
performance on two standard UDA benchmarks and also can be combined with recent
state-of-the-art method to achieve better performance.
