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Author (up) Emanuele Vivoli; Ali Furkan Biten; Andres Mafla; Dimosthenis Karatzas; Lluis Gomez edit   pdf
url  doi
  Title MUST-VQA: MUltilingual Scene-text VQA Type Conference Article
  Year 2022 Publication Proceedings European Conference on Computer Vision Workshops Abbreviated Journal  
  Volume 13804 Issue Pages 345–358  
  Keywords Visual question answering; Scene text; Translation robustness; Multilingual models; Zero-shot transfer; Power of language models  
  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.  
  Address Tel-Aviv; Israel; October 2022  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ECCVW  
  Notes DAG; 302.105; 600.155; 611.002 Approved no  
  Call Number Admin @ si @ VBM2022 Serial 3770  
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