MUST-VQA: MUltilingual Scene-text VQA

• URL: http://arxiv.org/abs/2209.06730v1
• Date: Wed, 14 Sep 2022 15:37:56 GMT
• Title: MUST-VQA: MUltilingual Scene-text VQA
• Authors: Emanuele Vivoli, Ali Furkan Biten, Andres Mafla, Dimosthenis Karatzas, Lluis Gomez
• Abstract summary: We consider the task of Scene Text Visual Question Answering (STVQA) in which the question can be asked in different languages. We show the effectiveness of adapting multilingual language models into STVQA tasks.
• Score: 7.687215328455748
• License: http://creativecommons.org/licenses/by/4.0/
• 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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