2024/06/09 by Guojin Cao, Cao, Guojin, Jiaxu Li +7
Computer Science · Decision Sciences · Environmental Science · #Big Data Technologies and Applications #Computational Physics and Python Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Hydrological Forecasting Using AI
paper · pdf · doi:10.48550/arxiv.2407.01579
openalex publication_date 2024/06/09 · openalex created_date 2024/07/06 · openalex updated_date 2026/07/28
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.