  In this report, we present our solution for the semantic segmentation in
adverse weather, in UG2+ Challenge at CVPR 2024. To achieve robust and accurate
segmentation results across various weather conditions, we initialize the
InternImage-H backbone with pre-trained weights from the large-scale joint
dataset and enhance it with the state-of-the-art Upernet segmentation method.
Specifically, we utilize offline and online data augmentation approaches to
extend the train set, which helps us to further improve the performance of the
segmenter. As a result, our proposed solution demonstrates advanced performance
on the test set and achieves 3rd position in this challenge.
