  Domain adaptive semantic segmentation refers to making predictions on a
certain target domain with only annotations of a specific source domain.
Current state-of-the-art works suggest that performing category alignment can
alleviate domain shift reasonably. However, they are mainly based on
image-to-image adversarial training and little consideration is given to
semantic variations of an object among images, failing to capture a
comprehensive picture of different categories. This motivates us to explore a
holistic representative, the semantic distribution from each category in source
domain, to mitigate the problem above. In this paper, we present semantic
distribution-aware contrastive adaptation algorithm that enables pixel-wise
representation alignment under the guidance of semantic distributions.
Specifically, we first design a pixel-wise contrastive loss by considering the
correspondences between semantic distributions and pixel-wise representations
from both domains. Essentially, clusters of pixel representations from the same
category should cluster together and those from different categories should
spread out. Next, an upper bound on this formulation is derived by involving
the learning of an infinite number of (dis)similar pairs, making it efficient.
Finally, we verify that SDCA can further improve segmentation accuracy when
integrated with the self-supervised learning. We evaluate SDCA on multiple
benchmarks, achieving considerable improvements over existing algorithms.The
code is publicly available at https://github.com/BIT-DA/SDCA
