mlsanityData to 7 Oct 2026

MUST-VQA: MUltilingual Scene-text VQA

arXiv 2209.06730 ↗First posted 14 Sep 2022cs.CVcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 31 of its 36 references that are in the map. First posted on arXiv 14 Sep 2022.

Builds on

31 of 36 references.

References · 31

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  5. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  6. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  7. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  8. 2016Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotationscited by 2,646
  9. 2020Shortcut Learning in Deep Neural Networkscited by 1,928
  10. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,813
  11. 2020GLU Variants Improve Transformercited by 1,724
  12. 2019Towards VQA Models That Can Readcited by 1,696
19 more
  1. 2016Enriching Word Vectors with Subword Informationcited by 1,578
  2. 2017Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answeringcited by 1,310
  3. 2020DocVQA: A Dataset for VQA on Document Imagescited by 1,288
  4. 2021InfographicVQAcited by 430
  5. 2020TextCaps: a Dataset for Image Captioning with Reading Comprehensioncited by 398
  6. 2019Scene Text Visual Question Answeringcited by 327
  7. 2019Iterative Answer Prediction with Pointer-Augmented Multimodal Transformers for TextVQAcited by 107
  8. 2020TAP: Text-Aware Pre-training for Text-VQA and Text-Captioncited by 95
  9. 2021LaTr: Layout-Aware Transformer for Scene-Text VQAcited by 72
  10. 2019Rosetta: Large scale system for text detection and recognition in imagescited by 69
  11. 2017BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languagescited by 52
  12. 2019ICDAR 2019 Competition on Scene Text Visual Question Answeringcited by 46
  13. 2020Spatially Aware Multimodal Transformers for TextVQAcited by 44
  14. 2022OCR-IDL: OCR Annotations for Industry Document Library Datasetcited by 35
  15. 2020Finding the Evidence: Localization-aware Answer Prediction for Text Visual Question Answeringcited by 19
  16. 2020Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCapscited by 18
  17. 2020Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Featurescited by 6
  18. 2020Multimodal grid features and cell pointers for Scene Text Visual Question Answeringcited by 5
  19. 2020An efficient automated data analytics approach to large scale computational comparative linguisticscited by 1

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Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2024MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answeringcited by 93
  2. 2023Visual Question Answering: A Survey on Techniques and Common Trends in Recent Literaturecited by 20
  3. 2024One missing piece in Vision and Language: A Survey on Comics Understandingcited by 8
  4. 2025KRETA: A Benchmark for Korean Reading and Reasoning in Text-Rich VQA Attuned to Diverse Visual Contextscited by 6