This technical report presents the implementation details of 2nd winning for
CVPR'24 UG2 WeatherProof Dataset Challenge. This challenge aims at semantic
segmentation of images degraded by various degrees of weather from all around
the world. We addressed this problem by introducing a pre-trained large-scale
vision foundation model: InternImage, and trained it using images with
different levels of noise. Besides, we did not use additional datasets in the
training procedure and utilized dense-CRF as post-processing in the final
testing procedure. As a result, we achieved 2nd place in the challenge with
45.1 mIOU and fewer submissions than the other winners.

本技术报告介绍了 CVPR'24 UG2 WeatherProof 数据集挑战的第二名实现细节，通过引入经过预训练的大规模视觉基础模型 InternImage，并使用具有不同噪声级别的图像进行训练，我们解决了图像天气退化语义分割问题，最终测试过程中使用 dense-CRF 作为后处理方式，结果以 45.1 mIOU 取得了第二名的成绩，且提交次数较其他获胜者少。