FogAdapt: Self-supervised domain adaptation for semantic segmentation of foggy images

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. La...

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Bibliographic Details
Main Authors:	Ali, M. (Author), Hafiz, R. (Author), Iqbal, J. (Author)
Format:	Article
Language:	English
Published:	Elsevier B.V. 2022
Subjects:	Adaptation methods Adverse weather article Condition Domain adaptation entropy Entropy Foggy scene understanding haze learning loss of function mutation Meteorology probability Scale invariance Scale-invariance Scale-invariant Scene understanding Self-supervised learning Semantic segmentation Semantic Segmentation Semantics smog uncertainty visibility Visibility weather
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FogAdapt: Self-supervised domain adaptation for semantic segmentation of foggy images

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