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MaXM: Towards Multilingual Visual Question Answering

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arxiv 2209.05401 v3 pith:ECQNKNX7 submitted 2022-09-12 cs.CL cs.CV

Soravit Changpinyo , Linting Xue , Michal Yarom , Ashish V. Thapliyal , Idan Szpektor , Julien Amelot , Xi Chen , Radu Soricut This is my paper · ORCID
classification cs.CLcs.CV
keywords multilingualansweringbenchmarkmvqaquestionvisualannotationapproach
verification ladder T0 review T1 audit T2 compute T3 formal
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Visual Question Answering (VQA) has been primarily studied through the lens of the English language. Yet, tackling VQA in other languages in the same manner would require a considerable amount of resources. In this paper, we propose scalable solutions to multilingual visual question answering (mVQA), on both data and modeling fronts. We first propose a translation-based framework to mVQA data generation that requires much less human annotation efforts than the conventional approach of directly collection questions and answers. Then, we apply our framework to the multilingual captions in the Crossmodal-3600 dataset and develop an efficient annotation protocol to create MaXM, a test-only VQA benchmark in 7 diverse languages. Finally, we develop a simple, lightweight, and effective approach as well as benchmark state-of-the-art English and multilingual VQA models. We hope that our benchmark encourages further research on mVQA.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. HW-MLVQA: Elucidating Multilingual Handwritten Document Understanding with a Comprehensive VQA Benchmark

    cs.CV 2025-07 reject novelty 6.0 of 10

    A handwritten bilingual VQA benchmark with about 1.5k pages and up to 4.8k question-answer pairs, plus baselines showing current models perform poorly, particularly with OCR-derived text.

  2. All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages

    cs.CV 2024-11 conditional novelty 6.0 of 10

    ALM-bench is a 100-language, 19-domain cultural visual QA benchmark on which GPT-4o reaches 78.8% and the best open model, GLM-4V-9B, reaches 51.9%.

  3. Benchmarking Multimodal Models for Ukrainian Language Understanding Across Academic and Cultural Domains

    cs.CL 2024-11 conditional novelty 6.0 of 10

    Introduces ZNO-Vision, a 4,306-item Ukrainian multimodal exam benchmark, plus a translated VQA set and a 20-dish cuisine test, and finds only Gemini, Claude, and Qwen2-VL-72B beat the chance baseline.

  4. Synthetic Document Question Answering in Hungarian

    cs.CV 2025-05 conditional novelty 4.0 of 10

    New Hungarian document VQA datasets (HuDocVQA, HuDocVQA-manual, HuCCPDF) reveal far lower accuracy for frontier VLMs than English DocVQA, with finetuning plus OCR data recovering part of the gap.

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