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FAFormer: Foggy Scene Semantic Segmentation by Fog-Invariant Auxiliary Domain Adaptation

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Image and Graphics (ICIG 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14355))

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Abstract

Semantic Segmentation in the Foggy Scenes (SSFS) remains a difficult problem due to uncertainties caused by imperfect observations. Considering the success of domain adaptive semantic segmentation in the clear scenes, we believe it is reasonable to transfer the knowledge from the clear images to the foggy images. Different from the previous methods which mainly focus on alignment between the clear domain and the foggy domain, we try to transfer the fog knowledge between different domains to a “teacher” segmentor, thus the latter can generate better pseudo labels to supervise the student segmentor (main segmentor) to close the domain gap. Our method achieved better performance on ACDC and Foggy Zurich benchmark compared with mainstream works.

Supported by National Natural Science Foundation of China under Grant 42071340 and Program of Song Shan Laboratory (included in the management of Major Science and Technology of Henan Province) under Grant 2211000211000-01.

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Authors and Affiliations

  1. PLA Strategic Support Force Information Engineering University, Zhengzhou, 450001, Henan, China

    Ziquan Wang & Zhipeng Jiang

Authors
  1. Ziquan Wang
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  2. Zhipeng Jiang
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Corresponding author

Correspondence to Ziquan Wang .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Huchuan Lu

  2. University of Sydney, Sydney, NSW, Australia

    Wanli Ouyang

  3. Shenzhen University, Shenzhen, China

    Hui Huang

  4. Tsinghua University, Beijing, China

    Jiwen Lu

  5. Dalian University of Technology, Dalian, China

    Risheng Liu

  6. Institute of Automation, CAS, Beijing, China

    Jing Dong

  7. University of Technology Sydney, Sydney, NSW, Australia

    Min Xu

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Wang, Z., Jiang, Z. (2023). FAFormer: Foggy Scene Semantic Segmentation by Fog-Invariant Auxiliary Domain Adaptation. In: Lu, H., et al. Image and Graphics. ICIG 2023. Lecture Notes in Computer Science, vol 14355. Springer, Cham. https://doi.org/10.1007/978-3-031-46305-1_4

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  • DOI: https://doi.org/10.1007/978-3-031-46305-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46304-4

  • Online ISBN: 978-3-031-46305-1

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