Deep Segmentation Domain Adaptation Network With Weighted Boundary Constraint

Semantic segmentation domain adaptation is used to deal with segmentation problems in a new domain even without pixel-level labels. Highly precise boundaries are the major indicator of segmentation performance, but the previous methods mainly have focused on global representation rather than on local representation, leading to an inferior performance of object boundaries in domain adaptation. In this paper, we propose weighted boundary constraint to refine those segmentation predictions and incorporate it into a generative adversarial network (GAN)-based network for domain adaption to achieve further significant improvement. The boundary constraint loss is designed as the cross-entropy between the intermediate result and the refined result. In addition, confidence from discriminator of GAN is used to constrain the boundary constraint loss to reduce the negative impact from inaccurate object boundaries. The entire network can be learned in an end-to-end manner. Both quantitative and qualitative experiments demonstrate the benefits of our approach which shows the competitive performance with the state-of-the-art methods.