Technical Report for CVPR 2024 WeatherProof Dataset Challenge: Semantic Segmentation on Paired Real Data

TL;DR

This report details the implementation of a semantic segmentation method for weather-degraded images, achieving second place in the CVPR 2024 challenge using a foundation model and noise training.

Contribution

Introduced a weather-robust semantic segmentation approach using InternImage and noise training without extra datasets, with effective post-processing.

Findings

Achieved 45.1 mIOU in the challenge

Did not use additional datasets for training

Utilized dense-CRF for post-processing

Abstract

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.