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European Conference on Computer Vision

ECCV 2020: Computer Vision – ECCV 2020 Workshops pp 666–670Cite as

Domain Generalization Using Shape Representation

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12535))

Abstract

CNN-based representations have greatly advanced the state of the art in visual recognition, but the community has primarily focused on the setting where training and test set belong to the same dataset/distribution

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References

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Acknowledgement

Our work was funded by UPitt CRDF, Google & Amazon.

Author information

Authors and Affiliations

  1. University of Pittsburgh, Pittsburgh, PA, 15260, USA

    Narges Honarvar Nazari & Adriana Kovashka

Authors
  1. Narges Honarvar Nazari
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  2. Adriana Kovashka
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Corresponding author

Correspondence to Narges Honarvar Nazari .

Editor information

Editors and Affiliations

  1. University of Clermont Auvergne, Clermont Ferrand, France

    Adrien Bartoli

  2. Università degli Studi di Udine, Udine, Italy

    Andrea Fusiello

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Honarvar Nazari, N., Kovashka, A. (2020). Domain Generalization Using Shape Representation. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12535. Springer, Cham. https://doi.org/10.1007/978-3-030-66415-2_45

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  • DOI: https://doi.org/10.1007/978-3-030-66415-2_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66414-5

  • Online ISBN: 978-3-030-66415-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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