During the last half decade, convolutional neural networks (CNNs) have
triumphed over semantic segmentation, which is one of the core tasks in many
applications such as autonomous driving and augmented reality. However, to
train CNNs requires a considerable amount of data, which is difficult to
collect and laborious to annotate. Recent advances in computer graphics make it
possible to train CNNs on photo-realistic synthetic imagery with
computer-generated annotations. Despite this, the domain mismatch between the
real images and the synthetic data hinders the models' performance. Hence, we
propose a curriculum-style learning approach to minimizing the domain gap in
urban scene semantic segmentation. The curriculum domain adaptation solves easy
tasks first to infer necessary properties about the target domain; in
particular, the first task is to learn global label distributions over images
and local distributions over landmark superpixels. These are easy to estimate
because images of urban scenes have strong idiosyncrasies (e.g., the size and
spatial relations of buildings, streets, cars, etc.). We then train a
segmentation network, while regularizing its predictions in the target domain
to follow those inferred properties. In experiments, our method outperforms the
baselines on two datasets and two backbone networks. We also report extensive
ablation studies about our approach.

在城市场景语义分割中，我们提出一种课程学习的方法来尽可能减小真实和合成数据之间的差距。该方法采用先易后难的策略，对全局标签分布和本地超像素标签分布进行先验知识的推断，并在此基础上训练语义分割网络。在两个数据集和两种 Backbone 网络上，我们的方法表现优于基线，并且进行了广泛的实验研究。

基于课程领域自适应的城市场景语义分割方法

A Curriculum Domain Adaptation Approach to the Semantic Segmentation of  Urban Scenes

During the last half decade, convolutional neural networks (CNNs) have
triumphed over semantic segmentation, which is one of the core tasks in many
applications such as autonomous driving. However, to train CNNs requires a
considerable amount of data, which is difficult to collect and laborious to
annotate. Recent advances in computer graphics make it possible to train CNNs
on photo-realistic synthetic imagery with computer-generated annotations.
Despite this, the domain mismatch between the real images and the synthetic
data cripples the models' performance. Hence, we propose a curriculum-style
learning approach to minimize the domain gap in urban scenery semantic
segmentation. The curriculum domain adaptation solves easy tasks first to infer
necessary properties about the target domain; in particular, the first task is
to learn global label distributions over images and local distributions over
landmark superpixels. These are easy to estimate because images of urban scenes
have strong idiosyncrasies (e.g., the size and spatial relations of buildings,
streets, cars, etc.). We then train a segmentation network while regularizing
its predictions in the target domain to follow those inferred properties. In
experiments, our method outperforms the baselines on two datasets and two
backbone networks. We also report extensive ablation studies about our
approach.

本论文介绍一种基于课程式学习（curriculum-style learning）的方法，通过学习全局标签分布和局部分布来缓解城市风景语义分割中真实图像和合成数据之间的领域差异，并在两个数据集和两个骨干网络上的实验中表现优异。