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.

翻译：视觉问答（VQA）主要通过英语视角进行研究。然而，以相同方式处理其他语言的VQA需要大量资源。在本文中，我们针对数据和建模两方面提出了多语言视觉问答（mVQA）的可扩展解决方案。首先，我们提出一种基于翻译的mVQA数据生成框架，与传统直接收集问题和答案的方法相比，该方法所需的人工标注工作量大幅减少。随后，我们将该框架应用于Crossmodal-3600数据集中的多语言字幕，并开发了一种高效的标注协议，构建了包含7种不同语言的仅测试用VQA基准数据集MaXM。最后，我们提出了一种简单、轻量且有效的方法，并对当前最先进的英文及多语言VQA模型进行了性能基准测试。希望我们的基准数据集能够推动mVQA领域的进一步研究。