Generalized Foggy-Scene Semantic Segmentation by Frequency Decoupling
Abstract
Foggy-scene semantic segmentation (FSSS) is highly challenging due to the diverse effects of fog on scene properties and the limited training data. Existing research has mainly focused on domain adaptation for FSSS, which has practical limitations when dealing with new scenes. In our paper, we introduce domain-generalized FSSS, which can work effectively on unknown distributions without extensive training. To address domain gaps, we propose a frequency decoupling (FreD) approach that separates fog-related effects (amplitude) from scene semantics (phase) in feature representations. Our method is compatible with both CNN and Vision Transformer backbones and outperforms existing approaches in various scenarios.
Cite
Text
Bi et al. "Generalized Foggy-Scene Semantic Segmentation by Frequency Decoupling." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024. doi:10.1109/CVPRW63382.2024.00146Markdown
[Bi et al. "Generalized Foggy-Scene Semantic Segmentation by Frequency Decoupling." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024.](https://mlanthology.org/cvprw/2024/bi2024cvprw-generalized/) doi:10.1109/CVPRW63382.2024.00146BibTeX
@inproceedings{bi2024cvprw-generalized,
title = {{Generalized Foggy-Scene Semantic Segmentation by Frequency Decoupling}},
author = {Bi, Qi and You, Shaodi and Gevers, Theo},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2024},
pages = {1389-1399},
doi = {10.1109/CVPRW63382.2024.00146},
url = {https://mlanthology.org/cvprw/2024/bi2024cvprw-generalized/}
}