Domain Adaptive Semantic Segmentation Through Structure Enhancement

Although fully convolutional networks have recently achieved great advances in semantic segmentation, the performance leaps heavily rely on supervision with pixel-level annotations which are extremely expensive and time-consuming to collect. Training models on synthetic data is a feasible way to relieve the annotation burden. However, the domain shift between synthetic and real images usually lead to poor generalization performance. In this work, we propose an effective method to adapt the segmentation network trained on synthetic images to real scenarios in an unsupervised fashion. To improve the adaptation performance for semantic segmentation, we enhance the structure information of the target images at both the feature level and the output level. Specifically, we enforce the segmentation network to learn a representation that encodes the target images' visual cues through image reconstruction, which is beneficial to the structured prediction of the target images. Further more, we implement adversarial training at the output space of the segmentation network to align the structured prediction of the source and target images based on the similar spatial structure they share. To validate the performance of our method, we conduct comprehensive experiments on the "GTA5 to Cityscapes" dataset which is a standard domain adaptation benchmark for semantic segmentation. The experimental results clearly demonstrate that our method can effectively bridge the synthetic and real image domains and obtain better adaptation performance compared with the existing state-of-the-art methods.