  Recent advances in unsupervised domain adaptation for semantic segmentation
have shown great potentials to relieve the demand of expensive per-pixel
annotations. However, most existing works address the domain discrepancy by
aligning the data distributions of two domains at a global image level whereas
the local consistencies are largely neglected. This paper presents an
innovative local contextual-relation consistent domain adaptation (CrCDA)
technique that aims to achieve local-level consistencies during the
global-level alignment. The idea is to take a closer look at region-wise
feature representations and align them for local-level consistencies.
Specifically, CrCDA learns and enforces the prototypical local
contextual-relations explicitly in the feature space of a labelled source
domain while transferring them to an unlabelled target domain via
backpropagation-based adversarial learning. An adaptive entropy max-min
adversarial learning scheme is designed to optimally align these hundreds of
local contextual-relations across domain without requiring discriminator or
extra computation overhead. The proposed CrCDA has been evaluated extensively
over two challenging domain adaptive segmentation tasks (e.g., GTA5 to
Cityscapes and SYNTHIA to Cityscapes), and experiments demonstrate its superior
segmentation performance as compared with state-of-the-art methods.
