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Robust visual recognition under adverse weather conditions is of greatimportance in real-world applications. In this context, we propose a new methodfor learning semantic segmentation models robust against fog. Its key idea isto consider the fog condition of an image as its style and close the gapbetween images with different fog conditions in neural style spaces of asegmentation model. In particular, since the neural style of an image is ingeneral affected by other factors as well as fog, we introduce a fog-passfilter module that learns to extract a fog-relevant factor from the style.Optimizing the fog-pass filter and the segmentation model alternately graduallycloses the style gap between different fog conditions and allows to learnfog-invariant features in consequence. Our method substantially outperformsprevious work on three real foggy image datasets. Moreover, it improvesperformance on both foggy and clear weather images, while existing methodsoften degrade performance on clear scenes.

FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation