Solution for CVPR 2024 UG2+ Challenge Track on All Weather Semantic Segmentation
<|reference_start|>Solution for CVPR 2024 UG2+ Challenge Track on All Weather Semantic Segmentation: 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.<|reference_end|>
@article{yu2024solution,
    title={Solution for CVPR 2024 UG2+ Challenge Track on All Weather Semantic
  Segmentation},
    author={Jun Yu, Yunxiang Zhang, Fengzhao Sun, Leilei Wang, Renjie Lu},
    journal={arXiv preprint arXiv:2406.05837},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05837},
    primaryClass={cs.CV cs.AI}
}