  Unsupervised domain adaptation (UDA) for semantic segmentation has been
well-studied in recent years. However, most existing works largely neglect the
local regional consistency across different domains and are less robust to
changes in outdoor environments. In this paper, we propose a novel and fully
end-to-end trainable approach, called regional contrastive consistency
regularization (RCCR) for domain adaptive semantic segmentation. Our core idea
is to pull the similar regional features extracted from the same location of
different images, i.e., the original image and augmented image, to be closer,
and meanwhile push the features from the different locations of the two images
to be separated. We innovatively propose a region-wise contrastive loss with
two sampling strategies to realize effective regional consistency. Besides, we
present momentum projection heads, where the teacher projection head is the
exponential moving average of the student. Finally, a memory bank mechanism is
designed to learn more robust and stable region-wise features under varying
environments. Extensive experiments on two common UDA benchmarks, i.e., GTAV to
Cityscapes and SYNTHIA to Cityscapes, demonstrate that our approach outperforms
the state-of-the-art methods.
