Traditional domain adaptive semantic segmentation addresses the task of
adapting a model to a novel target domain under limited or no additional
supervision. While tackling the input domain gap, the standard domain
adaptation settings assume no domain change in the output space. In semantic
prediction tasks, different datasets are often labeled according to different
semantic taxonomies. In many real-world settings, the target domain task
requires a different taxonomy than the one imposed by the source domain. We
therefore introduce the more general taxonomy adaptive cross-domain semantic
segmentation (TACS) problem, allowing for inconsistent taxonomies between the
two domains. We further propose an approach that jointly addresses the
image-level and label-level domain adaptation. On the label-level, we employ a
bilateral mixed sampling strategy to augment the target domain, and a
relabelling method to unify and align the label spaces. We address the
image-level domain gap by proposing an uncertainty-rectified contrastive
learning method, leading to more domain-invariant and class-discriminative
features. We extensively evaluate the effectiveness of our framework under
different TACS settings: open taxonomy, coarse-to-fine taxonomy, and
implicitly-overlapping taxonomy. Our approach outperforms the previous
state-of-the-art by a large margin, while being capable of adapting to target
taxonomies. Our implementation is publicly available at
this https URL

提出了更普适的交叉领域语义分割问题（TACS），该问题允许两个域之间的标签存在不一致性，并引入一种同时解决图像级和标签级适应性的方法，即采用双边混合采样策略增强目标域并使用标签重标记方法统一标签空间，同时提供一种基于对比学习的不确定性校正方法以减少域差异。大量实验结果表明该方法优于现有最优解，且能够适应目标分类学。