CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
This addresses the problem of cultural bias and limited language coverage in VQA for researchers and developers of multimodal AI, though it is incremental as it builds on existing VQA benchmarks by expanding diversity.
The authors tackled the lack of cultural and linguistic diversity in visual question answering (VQA) by constructing CVQA, a benchmark with culturally-driven images and questions from 30 countries in 31 languages, totaling 10k questions, and showed it is challenging for state-of-the-art multimodal models.
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 30 countries on four continents, covering 31 languages with 13 scripts, providing a total of 10k 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.