  Unsupervised domain adaptation is critical in various computer vision tasks,
such as object detection, instance segmentation, and semantic segmentation,
which aims to alleviate performance degradation caused by domain-shift. Most of
previous methods rely on a single-mode distribution of source and target
domains to align them with adversarial learning, leading to inferior results in
various scenarios. To that end, in this paper, we design a new spatial
attention pyramid network for unsupervised domain adaptation. Specifically, we
first build the spatial pyramid representation to capture context information
of objects at different scales. Guided by the task-specific information, we
combine the dense global structure representation and local texture patterns at
each spatial location effectively using the spatial attention mechanism. In
this way, the network is enforced to focus on the discriminative regions with
context information for domain adaption. We conduct extensive experiments on
various challenging datasets for unsupervised domain adaptation on object
detection, instance segmentation, and semantic segmentation, which demonstrates
that our method performs favorably against the state-of-the-art methods by a
large margin. Our source code is available at
https://isrc.iscas.ac.cn/gitlab/research/domain-adaption.
