  We propose a method to infer semantic segmentation maps from images captured
under adverse weather conditions. We begin by examining existing models on
images degraded by weather conditions such as rain, fog, or snow, and found
that they exhibit a large performance drop as compared to those captured under
clear weather. To control for changes in scene structures, we propose
WeatherProof, the first semantic segmentation dataset with accurate clear and
adverse weather image pairs that share an underlying scene. Through this
dataset, we analyze the error modes in existing models and found that they were
sensitive to the highly complex combination of different weather effects
induced on the image during capture. To improve robustness, we propose a way to
use language as guidance by identifying contributions of adverse weather
conditions and injecting that as "side information". Models trained using our
language guidance exhibit performance gains by up to 10.2% in mIoU on
WeatherProof, up to 8.44% in mIoU on the widely used ACDC dataset compared to
standard training techniques, and up to 6.21% in mIoU on the ACDC dataset as
compared to previous SOTA methods.
