MUST-VQA: MUltilingual Scene-Text VQA

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

Research output: Chapter in BookChapterResearchpeer-review


. 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.
Original languageEnglish
Title of host publicationComputer Vision – ECCV 2022 Workshops, Proceedings
EditorsLeonid Karlinsky, Tomer Michaeli, Ko Nishino
Number of pages14
ISBN (Electronic)978-3-031-25069-9
Publication statusPublished - Feb 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13804 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Multilingual models
  • Power of language models
  • Scene text
  • Translation robustness
  • Visual question answering
  • Zero-shot transfer

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