Unsupervised Domain Adaptation for Mobile Semantic Segmentation based on Cycle Consistency and Feature Alignment Abstract   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 MobileNetv2 architecture that performs the semantic segmentation task. An additional couple of discriminators, working at the feature level of the MobileNetv2, 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 endtoend 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 MobileNetv2 architecture allows its deployment on devices with limited computational resources as the ones employed in autonomous vehicles.

Semantic Segmentation via Domain Adaptation with Global Structure Embedding In this paper we focus on the problem of unsupervised domain adaptation for semantic segmentation. The previous works usually focus on adversarial learning either in pixellevel or featurelevel. However, global structure knowledge is often neglected in the adversarial learning due to the possible reasons: First, the result of pixellevel adversarial learning does not necessarily preserve the semantic consistency of the input image. Second, global structure knowledge is not embedded to regularize the featurelevel adversarial learning. In this work, we propose a framework for unsupervised domain adaptation in semantic segmentation which effectively incorporates pixel level, featurelevel adversarial learning and selftraining strategy. Our framework embeds the global structure knowledge into the adversarial training step to tackle the problem of structure misalignment. Consequently, our proposed framework achieves the stateoftheart semantic segmentation domain adaptation results on the task of transferring GTA5 to Cityscapes.