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Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12372))

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Abstract

Unsupervised domain adaptation enables to alleviate the need for pixel-wise annotation in the semantic segmentation. One of the most common strategies is to translate images from the source domain to the target domain and then align their marginal distributions in the feature space using adversarial learning. However, source-to-target translation enlarges the bias in translated images and introduces extra computations, owing to the dominant data size of the source domain. Furthermore, consistency of the joint distribution in source and target domains cannot be guaranteed through global feature alignment. Here, we present an innovative framework, designed to mitigate the image translation bias and align cross-domain features with the same category. This is achieved by 1) performing the target-to-source translation and 2) reconstructing both source and target images from their predicted labels. Extensive experiments on adapting from synthetic to real urban scene understanding demonstrate that our framework competes favorably against existing state-of-the-art methods.

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Acknowledgments

This work was partially supported by US National Science Foundation IIS-1718853, the CAREER grant IIS-1553687 and Cancer Prevention and Research Institute of Texas (CPRIT) award (RP190107).

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Authors and Affiliations

  1. University of Texas at Arlington, Arlington, Texas, USA

    Jinyu Yang, Weizhi An, Sheng Wang, Xinliang Zhu, Chaochao Yan & Junzhou Huang

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  1. Jinyu Yang
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  2. Weizhi An
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  3. Sheng Wang
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  4. Xinliang Zhu
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  6. Junzhou Huang
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Correspondence to Junzhou Huang .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Yang, J., An, W., Wang, S., Zhu, X., Yan, C., Huang, J. (2020). Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12372. Springer, Cham. https://doi.org/10.1007/978-3-030-58583-9_29

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  • DOI: https://doi.org/10.1007/978-3-030-58583-9_29

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