Papers › FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation

FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation

4 Apr 2022CVPR 2022 1arXiv:2204.01587archive 2025-07-28

Sohyun Lee, Taeyoung Son, Suha Kwak

Robust visual recognition under adverse weather conditions is of great importance in real-world applications. In this context, we propose a new method for learning semantic segmentation models robust against fog. Its key idea is to consider the fog condition of an image as its style and close the gap between images with different fog conditions in neural style spaces of a segmentation model. In particular, since the neural style of an image is in general affected by other factors as well as fog, we introduce a fog-pass filter module that learns to extract a fog-relevant factor from the style. Optimizing the fog-pass filter and the segmentation model alternately gradually closes the style gap between different fog conditions and allows to learn fog-invariant features in consequence. Our method substantially outperforms previous work on three real foggy image datasets. Moreover, it improves performance on both foggy and clear weather images, while existing methods often degrade performance on clear scenes.

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sohyun-l/fifo officialmentioned on GitHubpytorch report

sohyun-l/ExLPose mentioned on GitHubpytorch report

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Tasks

Domain Adaptation Foggy Scene Segmentation Scene Segmentation Segmentation Semantic Segmentation Unsupervised Domain Adaptation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Domain Adaptation	Cityscapes-to-FoggyDriving	FIFO	mIoU	50.7	#4 of 5	Archive leaderboard	report
Domain Adaptation	Cityscapes-to-FoggyZurich	FIFO	mIoU	48.4	#5 of 6	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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