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

MLAN: Multi-Level Adversarial Network for Domain Adaptive Semantic  Segmentation

Convolutional neural network-based approaches for semantic segmentation rely
on supervision with pixel-level ground truth, but may not generalize well to
unseen image domains. As the labeling process is tedious and labor intensive,
developing algorithms that can adapt source ground truth labels to the target
domain is of great interest. In this paper, we propose an adversarial learning
method for domain adaptation in the context of semantic segmentation.
Considering semantic segmentations as structured outputs that contain spatial
similarities between the source and target domains, we adopt adversarial
learning in the output space. To further enhance the adapted model, we
construct a multi-level adversarial network to effectively perform output space
domain adaptation at different feature levels. Extensive experiments and
ablation study are conducted under various domain adaptation settings,
including synthetic-to-real and cross-city scenarios. We show that the proposed
method performs favorably against the state-of-the-art methods in terms of
accuracy and visual quality.

本文提出了一种基于对抗学习的语义分割领域适应方法，该方法采用输出空间的对抗学习，并构建了多层对抗网络来有效地执行不同特征层面的输出空间域适应。在各种域适应设置下进行了大量实验和消融研究，并展示出该方法在准确性和视觉质量方面表现优越。