  This paper presents FogAdapt, a novel approach for domain adaptation of
semantic segmentation for dense foggy scenes. Although significant research has
been directed to reduce the domain shift in semantic segmentation, adaptation
to scenes with adverse weather conditions remains an open question. Large
variations in the visibility of the scene due to weather conditions, such as
fog, smog, and haze, exacerbate the domain shift, thus making unsupervised
adaptation in such scenarios challenging. We propose a self-entropy and
multi-scale information augmented self-supervised domain adaptation method
(FogAdapt) to minimize the domain shift in foggy scenes segmentation. Supported
by the empirical evidence that an increase in fog density results in high
self-entropy for segmentation probabilities, we introduce a self-entropy based
loss function to guide the adaptation method. Furthermore, inferences obtained
at different image scales are combined and weighted by the uncertainty to
generate scale-invariant pseudo-labels for the target domain. These
scale-invariant pseudo-labels are robust to visibility and scale variations. We
evaluate the proposed model on real clear-weather scenes to real foggy scenes
adaptation and synthetic non-foggy images to real foggy scenes adaptation
scenarios. Our experiments demonstrate that FogAdapt significantly outperforms
the current state-of-the-art in semantic segmentation of foggy images.
Specifically, by considering the standard settings compared to state-of-the-art
(SOTA) methods, FogAdapt gains 3.8% on Foggy Zurich, 6.0% on Foggy
Driving-dense, and 3.6% on Foggy Driving in mIoU when adapted from Cityscapes
to Foggy Zurich.
