Recent progresses in domain adaptive semantic segmentation demonstrate the effectiveness of adversarial learning (AL) in unsupervised domain adaptation. However, most adversarial learning based methods align source and target distributions at a global image level but neglect the inconsistency around local image regions. This paper presents a novel multi-level adversarial network (MLAN) that aims to address inter-domain inconsistency at both global image level and local region level optimally. MLAN has two novel designs, namely, region-level adversarial learning (RL-AL) and co-regularized adversarial learning (CR-AL). Specifically, RL-AL models prototypical regional context-relations explicitly in the feature space of a labelled source domain and transfers them to an unlabelled target domain via adversarial learning. CR-AL fuses region-level AL and image-level AL optimally via mutual regularization. In addition, we design a multi-level consistency map that can guide domain adaptation in both input space ($i.e.$, image-to-image translation) and output space ($i.e.$, self-training) effectively. Extensive experiments show that MLAN outperforms the state-of-the-art with a large margin consistently across multiple datasets.

本文提出了一种新型的多级对抗网络 (MLAN)，针对源域和目标域之间的全局图像水平和局部图像区域之间的不一致性，通过区域级对抗性学习 (RL-AL) 和共正则化对抗性学习 (CR-AL)，以及在输入空间和输出空间上的多级一致性映射来实现领域自适应。大量实验表明，MLAN 在多个数据集上一致地优于现有技术。

MLAN：用于域自适应语义分割的多层对抗网络