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International Conference on Theory and Application of Diagrams

Diagrams 2021: Diagrammatic Representation and Inference pp 348–352Cite as

Can Humans and Machines Classify Photographs as Depicting Negation?

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12909))

Abstract

How logical concepts such as negation can be visually represented is of central importance in the study of diagrammatic reasoning. To explore various ways in which negation can be visually represented, this study focuses on photographs as instances of purely visual representations. We use real-world photographic image data and study how well humans can classify those images as depicting negation. We also compare the human performance with a state-of-the-art machine (deep) learning model on this classification task. The present paper gives some preliminary results on our data-driven analyses.

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Acknowledgments

This work was supported by JSPS KAKENHI Grant Number JP20K12782 to the first author.

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Authors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Yuri Sato

  2. Keio University, Tokyo, Japan

    Koji Mineshima

Authors
  1. Yuri Sato
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  2. Koji Mineshima
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Corresponding author

Correspondence to Yuri Sato .

Editor information

Editors and Affiliations

  1. Jadavpur University, Kolkata, India

    Amrita Basu

  2. University of Cambridge, Cambridge, UK

    Gem Stapleton

  3. Lancaster University in Leipzig, Leipzig, Germany

    Sven Linker

  4. Deakin University, Burwood, VIC, Australia

    Catherine Legg

  5. Kyoto University, Kyoto, Japan

    Emmanuel Manalo

  6. Universidade Federal Fluminense, Niterói, Brazil

    Petrucio Viana

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Sato, Y., Mineshima, K. (2021). Can Humans and Machines Classify Photographs as Depicting Negation?. In: Basu, A., Stapleton, G., Linker, S., Legg, C., Manalo, E., Viana, P. (eds) Diagrammatic Representation and Inference. Diagrams 2021. Lecture Notes in Computer Science(), vol 12909. Springer, Cham. https://doi.org/10.1007/978-3-030-86062-2_35

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  • DOI: https://doi.org/10.1007/978-3-030-86062-2_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86061-5

  • Online ISBN: 978-3-030-86062-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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