  This report describes the winning solution to the WeatherProof Dataset
Challenge (CVPR 2024 UG2+ Track 3). Details regarding the challenge are
available at https://cvpr2024ug2challenge.github.io/track3.html. We propose an
enhanced semantic segmentation pipeline for this challenge. Firstly, we improve
semantic segmentation models, using backbone pretrained with Depth Anything to
improve UperNet model and SETRMLA model, and adding language guidance based on
both weather and category information to InternImage model. Secondly, we
introduce a new dataset WeatherProofExtra with wider viewing angle and employ
data augmentation methods, including adverse weather and super-resolution.
Finally, effective training strategies and ensemble method are applied to
improve final performance further. Our solution is ranked 1st on the final
leaderboard. Code will be available at
https://github.com/KaneiGi/WeatherProofChallenge.
