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
this https URL

本文提出使用有别于过往方法的对比公式来实现 CONtrastive FEaTure and pIxel alignment (CONFETI)，并使用类别间跨领域信息将像素级和特征级（采用像素原型对比方法）的对齐相结合，以用于解决合成和真实世界数据集之间的领域缺口问题，在 DeepLabV2 上优于现有最先进方法。