  Learning segmentation from synthetic data and adapting to real data can
significantly relieve human efforts in labelling pixel-level masks. A key
challenge of this task is how to alleviate the data distribution discrepancy
between the source and target domains, i.e. reducing domain shift. The common
approach to this problem is to minimize the discrepancy between feature
distributions from different domains through adversarial training. However,
directly aligning the feature distribution globally cannot guarantee
consistency from a local view (i.e. semantic-level), which prevents certain
semantic knowledge learned on the source domain from being applied to the
target domain. To tackle this issue, we propose a semi-supervised approach
named Alleviating Semantic-level Shift (ASS), which can successfully promote
the distribution consistency from both global and local views. Specifically,
leveraging a small number of labeled data from the target domain, we directly
extract semantic-level feature representations from both the source and the
target domains by averaging the features corresponding to same categories
advised by pixel-level masks. We then feed the produced features to the
discriminator to conduct semantic-level adversarial learning, which
collaborates with the adversarial learning from the global view to better
alleviate the domain shift. We apply our ASS to two domain adaptation tasks,
from GTA5 to Cityscapes and from Synthia to Cityscapes. Extensive experiments
demonstrate that: (1) ASS can significantly outperform the current unsupervised
state-of-the-arts by employing a small number of annotated samples from the
target domain; (2) ASS can beat the oracle model trained on the whole target
dataset by over 3 points by augmenting the synthetic source data with annotated
samples from the target domain without suffering from the prevalent problem of
overfitting to the source domain.
