Unsupervised Domain Adaptation for Semantic Segmentation of Urban Scenes

Abstract

The semantic understanding of urban scenes is one of the key components for an autonomous driving system. Complex deep neural networks for this task require to be trained with a huge amount of labeled data, which is difficult and expensive to acquire. A recently proposed workaround is the usage of synthetic data, however the differences between real world and synthetic scenes limit the performance. We propose an unsupervised domain adaptation strategy to adapt a synthetic supervised training to real world data. The proposed learning strategy exploits three components: a standard supervised learning on synthetic data, an adversarial learning strategy able to exploit both labeled synthetic data and unlabeled real data and finally a self-teaching strategy working on unlabeled data only. The last component is guided by the segmentation confidence, estimated by the fully convolutional discriminator of the adversarial learning module, helping to further reduce the domain shift between synthetic and real data. Furthermore we weighted this loss on the basis of the class frequencies to enhance the performance on less common classes. Experimental results prove the effectiveness of the proposed strategy in adapting a segmentation network trained on synthetic datasets, like GTA5 and SYNTHIA, to a real dataset as Cityscapes.