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Multimedia Systems
Erschienen in: Multimedia Systems 3/2023

28.02.2023 | Regular Paper

MLCB-Net: a multi-level class balancing network for domain adaptive semantic segmentation

verfasst von: Wei Li, Xiwei Yang, Zhixin Li

Erschienen in: Multimedia Systems | Ausgabe 3/2023

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Abstract

To solve the long-tail distribution problem in domain adaptive semantic segmentation, we propose a novel multilevel class balancing network (MLCB-Net). We adapt a novel frequency fusion module (FFM) by using prior knowledge to guide the domain adaptive semantic segmentation network, thus carrying out regular constraints in global training. Furthermore, in the domain adaptation process, we introduce a dual-branch balancing module (DBBM) to resample the class-level features, which makes the model not only improve the sensitivity to low-frequency classes but also does not damage the feature representation ability of the classifier. In addition, we combine self-supervised learning strategies with our proposed modules to further improve segmentation performance. Experiments on two baseline tasks, GTA5 to Cityscapes and SYNTHIA to Cityscapes, show that MLCB-Net achieves a new state-of-the-art benchmark and is more robust.
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Metadaten
Titel
MLCB-Net: a multi-level class balancing network for domain adaptive semantic segmentation
verfasst von
Wei Li
Xiwei Yang
Zhixin Li
Publikationsdatum
28.02.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
Multimedia Systems / Ausgabe 3/2023
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-023-01055-4

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