Unsupervised domain extension for nighttime semantic segmentation in urban scenes

  • This paper deals with the problem of semantic image segmentation of street scenes at night, as the recent advances in semantic image segmentation are mainly related to daytime images. We propose a method to extend the learned domain of daytime images to nighttime images based on an extended version of the CycleGAN framework and its integration into a self-supervised learning framework. The aim of the method is to reduce the cost of human annotation of night images by robustly transferring images from day to night and training the segmentation network to make consistent predictions in both domains, allowing the usage of completely unlabelled images in training. Experiments show that our approach significantly improves the performance on nighttime images while keeping the performance on daytime images stable. Furthermore, our method can be applied to many other problem formulations and is not specifically designed for semantic segmentation.

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Author:Sebastian SchererGND, Robin SchönGND, Katja LudwigGND, Rainer LienhartGND
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/86939
Parent Title (English):Proceedings of the 2nd International Conference on Deep Learning Theory and Applications (DeLTA 2021)
Place of publication:Setúbal
Editor:Ana Fred, Carlo Sansone, Kurosh Madani
Type:Part of a Book
Year of first Publication:2021
Publishing Institution:Universität Augsburg
Release Date:2021/05/19
First Page:38
Last Page:47
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Maschinelles Lernen und Maschinelles Sehen
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY-NC-ND 4.0: Creative Commons: Namensnennung - Nicht kommerziell - Keine Bearbeitung (mit Print on Demand)