Knowledge based domain adaptation for semantic segmentation

Domain adaptation for semantic segmentation is a challenging problem for two reasons. One reason is that annotating labels is an extremely high cost work. Another reason is that the domain gap between the source and target domains limits the performance of semantic segmentation. In this paper, we propose an unsupervised knowledge based domain adaptation method for semantic segmentation. The proposed method consists of three steps. First, the common knowledge is loaded from the source and target domains. Then, the loaded knowledge is filtered according to the specific input image. In the end, the filtered knowledge is fused with the high-level features to guide domain adaptation. Our main contributions are: (1) a first novel knowledge based domain adaptation approach for semantic segmentation and (2) a triangular constraint for knowledge loading, in which the semantic vectors are smoothly imported. Experimental results on three datasets indicate that our method achieves competitive results in some scenarios compared with the state-of-the-art approaches.