  In this paper, we consider the problem of unsupervised domain adaptation in
the semantic segmentation. There are two primary issues in this field, i.e.,
what and how to transfer domain knowledge across two domains. Existing methods
mainly focus on adapting domain-invariant features (what to transfer) through
adversarial learning (how to transfer). Context dependency is essential for
semantic segmentation, however, its transferability is still not well
understood. Furthermore, how to transfer contextual information across two
domains remains unexplored. Motivated by this, we propose a cross-attention
mechanism based on self-attention to capture context dependencies between two
domains and adapt transferable context. To achieve this goal, we design two
cross-domain attention modules to adapt context dependencies from both spatial
and channel views. Specifically, the spatial attention module captures local
feature dependencies between each position in the source and target image. The
channel attention module models semantic dependencies between each pair of
cross-domain channel maps. To adapt context dependencies, we further
selectively aggregate the context information from two domains. The superiority
of our method over existing state-of-the-art methods is empirically proved on
"GTA5 to Cityscapes" and "SYNTHIA to Cityscapes".
