This technical report presents our team's solution for the WeatherProof
Dataset Challenge: Semantic Segmentation in Adverse Weather at CVPR'24 UG2+. We
propose a two-stage deep learning framework for this task. In the first stage,
we preprocess the provided dataset by concatenating images into video
sequences. Subsequently, we leverage a low-rank video deraining method to
generate high-fidelity pseudo ground truths. These pseudo ground truths offer
superior alignment compared to the original ground truths, facilitating model
convergence during training. In the second stage, we employ the InternImage
network to train for the semantic segmentation task using the generated pseudo
ground truths. Notably, our meticulously designed framework demonstrates
robustness to degraded data captured under adverse weather conditions. In the
challenge, our solution achieved a competitive score of 0.43 on the Mean
Intersection over Union (mIoU) metric, securing a respectable rank of 4th.

我们团队利用两阶段深度学习框架，在恶劣天气条件下的语义分割任务中通过预处理数据、生成高保真度地面真值，以及应用 InternImage 网络等方法，成功获得了 0.43 的平均交并比（mIoU）竞赛得分，在竞赛中获得了第四名的可观成绩。