  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.
