Rainy-condition domain adaptation notes for semantic segmentation, centered on the ICMEW 2023 paper Comprehensive Augmented Domain Adaptation for Image Segmentation Under Rainy Conditions.
Preview image source: the public ICMEW 2023 proceedings table of contents PDF at https://www.proceedings.com/content/070/070376webtoc.pdf.
Rainy scenes create a practical domain gap for perception models: synthetic or clear-weather segmentation models often fail when rain streaks, haze, water reflections, and low visibility shift both texture and class boundaries. RainyDA keeps the paper identity, citation, and project notes in one lightweight place.
- Title: Comprehensive Augmented Domain Adaptation for Image Segmentation Under Rainy Conditions
- Venue: IEEE ICME Workshops 2023
- Pages: 63-68
- DOI: https://doi.org/10.1109/ICMEW59549.2023.00017
- DBLP: https://dblp.org/rec/conf/icmcs/LiuXHY023
README.md project overview
assets/icmew-toc-page.png proceedings preview image
@inproceedings{liu2023comprehensive,
title = {Comprehensive Augmented Domain Adaptation for Image Segmentation Under Rainy Conditions},
author = {Liu, Minghao and Xie, Jiaxuan and Hu, Yuzhang and Yang, Wenhan and Liu, Jiaying},
booktitle = {2023 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)},
pages = {63--68},
year = {2023},
publisher = {IEEE}
}