We consider the problem of unsupervised domain adaptation in semantic
segmentation. The key in this campaign consists in reducing the domain shift,
i.e., enforcing the data distributions of the two domains to be similar. A
popular strategy is to align the marginal distribution in the feature space
through adversarial learning. However, this global alignment strategy does not
consider the local category-level feature distribution. A possible consequence
of the global movement is that some categories which are originally well
aligned between the source and target may be incorrectly mapped. To address
this problem, this paper introduces a category-level adversarial network,
aiming to enforce local semantic consistency during the trend of global
alignment. Our idea is to take a close look at the category-level data
distribution and align each class with an adaptive adversarial loss.
Specifically, we reduce the weight of the adversarial loss for category-level
aligned features while increasing the adversarial force for those poorly
aligned. In this process, we decide how well a feature is category-level
aligned between source and target by a co-training approach. In two domain
adaptation tasks, i.e., GTA5 -> Cityscapes and SYNTHIA -> Cityscapes, we
validate that the proposed method matches the state of the art in segmentation
accuracy.

本文提出了一种基于分类级别对抗网络和协同训练方法的非监督域自适应语义分割模型，通过加强对局部语义一致性的控制来优化全局对齐策略，取得了与当前领先技术水平相当的分割精度。实验任务包括 GTA5->Cityscapes 和 SYNTHIA -> Cityscapes 两种不同的领域自适应任务。