Contrast, Stylize and Adapt: Unsupervised Contrastive Learning Framework
  for Domain Adaptive Semantic Segmentation
Tianyu Li Subhankar Roy Huayi Zhou Hongtao Lu Stephane Lathuiliere
To overcome the domain gap between synthetic and real-world datasets,
unsupervised domain adaptation methods have been proposed for semantic
segmentation. Majority of the previous approaches have attempted to reduce the
gap either at the pixel or feature level, disregarding the fact that the two
components interact positively. To address this, we present CONtrastive FEaTure
and pIxel alignment (CONFETI) for bridging the domain gap at both the pixel and
feature levels using a unique contrastive formulation. We introduce
well-estimated prototypes by including category-wise cross-domain information
to link the two alignments: the pixel-level alignment is achieved using the
jointly trained style transfer module with the prototypical semantic
consistency, while the feature-level alignment is enforced to cross-domain
features with the \textbf{pixel-to-prototype contrast}. Our extensive
experiments demonstrate that our method outperforms existing state-of-the-art
methods using DeepLabV2. Our code is available at
https://github.com/cxa9264/CONFETI