The supervised training of deep networks for semantic segmentation requires a
huge amount of labeled real world data. To solve this issue, a commonly
exploited workaround is to use synthetic data for training, but deep networks
show a critical performance drop when analyzing data with slightly different
statistical properties with respect to the training set. In this work, we
propose a novel Unsupervised Domain Adaptation (UDA) strategy to address the
domain shift issue between real world and synthetic representations. An
adversarial model, based on the cycle consistency framework, performs the
mapping between the synthetic and real domain. The data is then fed to a
MobileNet-v2 architecture that performs the semantic segmentation task. An
additional couple of discriminators, working at the feature level of the
MobileNet-v2, allows to better align the features of the two domain
distributions and to further improve the performance. Finally, the consistency
of the semantic maps is exploited. After an initial supervised training on
synthetic data, the whole UDA architecture is trained end-to-end considering
all its components at once. Experimental results show how the proposed strategy
is able to obtain impressive performance in adapting a segmentation network
trained on synthetic data to real world scenarios. The usage of the lightweight
MobileNet-v2 architecture allows its deployment on devices with limited
computational resources as the ones employed in autonomous vehicles.

本研究提出了一种基于无监督域自适应（UDA）策略的语义分割方法，该方法利用对抗性模型实现了合成域和真实域之间的映射，利用 MobileNet-v2 架构实现了语义分割，并在特征级别增加了两个辨别器以提高性能。经实验证明，该方法能够在将分割网络训练为适应于真实世界场景时取得优异的性能。