This report describes the winning solution to the WeatherProof Dataset
Challenge (CVPR 2024 UG2+ Track 3). Details regarding the challenge are
available at this https URL 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
this https URL

描述第一名解决方案，提出了一种增强的语义分割管道，通过使用经过深度预训练的骨干模型、语言引导和数据增强方法来改进天气下的语义分割模型，并采用有效的训练策略和集成方法来进一步提高性能。