SDAT-Former: Foggy Scene Semantic Segmentation Via A Strong Domain Adaptation Teacher

Semantic Segmentation in the Foggy Scenes (SSFS) remains a difficult problem due to uncertainties caused by imperfect observations. Considering the success of domain adaptive semantic segmentation in the clear scenes, we believe it is reasonable to transfer the knowledge from the clear images to the foggy images. Different from the previous methods which mainly focus on narrowing the domain gap caused by fog, we try to transfer both the knowledge of fog factors and style factors between different domains to a "teacher" seg-mentor, thus the latter can generate better pseudo labels to supervise the student segmentor (main segmentor) to close the domain gap. Our method achieved better performance on ACDC and Foggy Zurich benchmark compared with state-of-the-art works.