  Most state-of-the-art semantic segmentation approaches only achieve high
accuracy in good conditions. In practically-common but less-discussed adverse
environmental conditions, their performance can decrease enormously. Existing
studies usually cast the handling of segmentation in adverse conditions as a
separate post-processing step after signal restoration, making the segmentation
performance largely depend on the quality of restoration. In this paper, we
propose a novel deep-learning framework to tackle semantic segmentation and
image restoration in adverse environmental conditions in a holistic manner. The
proposed approach contains two components: Semantically-Guided Adaptation,
which exploits semantic information from degraded images to refine the
segmentation; and Exemplar-Guided Synthesis, which restores images from
semantic label maps given degraded exemplars as the guidance. Our method
cooperatively leverages the complementarity and interdependence of low-level
restoration and high-level segmentation in adverse environmental conditions.
Extensive experiments on various datasets demonstrate that our approach can not
only improve the accuracy of semantic segmentation with degradation cues, but
also boost the perceptual quality and structural similarity of image
restoration with semantic guidance.
