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European Conference on Computer Vision

ECCV 2022: Computer Vision – ECCV 2022 Workshops pp 345–358Cite as

MUST-VQA: MUltilingual Scene-Text VQA

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13804))

Abstract

In this paper, we present a framework for Multilingual Scene Text Visual Question Answering that deals with new languages in a zero-shot fashion. Specifically, we consider the task of Scene Text Visual Question Answering (STVQA) in which the question can be asked in different languages and it is not necessarily aligned to the scene text language. Thus, we first introduce a natural step towards a more generalized version of STVQA: MUST-VQA. Accounting for this, we discuss two evaluation scenarios in the constrained setting, namely IID and zero-shot and we demonstrate that the models can perform on a par on a zero-shot setting. We further provide extensive experimentation and show the effectiveness of adapting multilingual language models into STVQA tasks.

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Notes

  1. 1.

    https://www.ethnologue.com/enterprise-faq/how-many-languages-world-are-unwritten-0.

  2. 2.

    cloud.google.com/translate/.

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Acknowledgments

This work has been supported by projects PDC2021-121512-I00, PLEC2021-00785, PID2020-116298GB-I00, ACE034/21/000084, the CERCA Programme/Generalitat de Catalunya, AGAUR project 2019PROD00090 (BeARS), the Ramon y Cajal RYC2020-030777-I/AEI/10.13039/501100011033 and PhD scholarship from UAB (B18P0073).

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Authors and Affiliations

  1. University of Florence, Florence, Italy

    Emanuele Vivoli

  2. Computer Vision Center, UAB, Barcelona, Spain

    Emanuele Vivoli, Ali Furkan Biten, Andres Mafla, Dimosthenis Karatzas & Lluis Gomez

Authors
  1. Emanuele Vivoli
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  2. Ali Furkan Biten
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  3. Andres Mafla
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  4. Dimosthenis Karatzas
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  5. Lluis Gomez
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Corresponding author

Correspondence to Emanuele Vivoli .

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Editors and Affiliations

  1. IBM Research - MIT-IBM Watson AI Lab, Massachusetts, USA

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Cite this paper

Vivoli, E., Biten, A.F., Mafla, A., Karatzas, D., Gomez, L. (2023). MUST-VQA: MUltilingual Scene-Text VQA. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13804. Springer, Cham. https://doi.org/10.1007/978-3-031-25069-9_23

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  • DOI: https://doi.org/10.1007/978-3-031-25069-9_23

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