Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge present in both visual and textual data. However, most of the current VQA models use datasets that are primarily focused on English and a few major world languages, with images that are typically Western-centric. While recent efforts have tried to increase the number of languages covered on VQA datasets, they still lack diversity in low-resource languages. More importantly, although these datasets often extend their linguistic range via translation or some other approaches, they usually keep images the same, resulting in narrow cultural representation. To address these limitations, we construct CVQA, a new Culturally-diverse multilingual Visual Question Answering benchmark, designed to cover a rich set of languages and cultures, where we engage native speakers and cultural experts in the data collection process. As a result, CVQA includes culturally-driven images and questions from across 28 countries on four continents, covering 26 languages with 11 scripts, providing a total of 9k questions. We then benchmark several Multimodal Large Language Models (MLLMs) on CVQA, and show that the dataset is challenging for the current state-of-the-art models. This benchmark can serve as a probing evaluation suite for assessing the cultural capability and bias of multimodal models and hopefully encourage more research efforts toward increasing cultural awareness and linguistic diversity in this field.

构建了CVQA，一个新的多元文化多语言视觉问答基准，覆盖了28个国家的文化驱动图像和问题，包括26种语言和11种文字，共提供了9k个问题。针对CVQA对几种多模态大型语言模型进行了基准测试，显示出该数据集对当前最先进的模型具有挑战性。这一基准测试可以作为评估多模态模型的文化能力和偏见的探测套件，并希望鼓励更多研究努力来增加该领域中的文化意识和语言多样性。

CVQA：跨文化多语言视觉问答基准