Synthetic to Realistic Imbalanced Domain Adaption for Urban Scene Perception

Deep neural networks technique has achieved impressive performance on semantic segmentation, while its training process requires a large amount of pixel-wise labeled data. Domain adaptation, as a promising solution, can break the restriction by training the model on synthetic data, and generalizing it in real-world data. However, there is still a lack of attention paid to the imbalance problems on semantic segmentation adaptation, including the imbalance problem between 1) source and target data and 2) different classes. To solve these problems, a progressive hierarchical feature alignment method is proposed in this article. To alleviate the data imbalance problem, the network is progressively trained by the data from multisource domains, so as to obtain domain-invariant features. To address the class imbalance problem, the features are aligned hierarchically across domains. According to the experimental results, our method shows the competitive adapted segmentation performance on three benchmark datasets.