CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
- David Romero ,
- Chenyang Lyu ,
- Haryo Akbarianto Wibowo ,
- Teresa Lynn ,
- Injy Hamed ,
- Aditya Nanda Kishore ,
- Aishik Mandal ,
- Alina Dragonetti ,
- Artem Abzaliev ,
- A. Tonja ,
- Bontu Fufa Balcha ,
- Chenxi Whitehouse ,
- Christian Salamea ,
- Dan John Velasco ,
- D. Adelani ,
- D. Meur ,
- Emilio Villa-Cueva ,
- Fajri Koto ,
- Fauzan Farooqui ,
- Frederico Belcavello ,
- Ganzorig Batnasan ,
- Gisela Vallejo ,
- Grainne Caulfield ,
- Guido Ivetta ,
- Haiyue Song ,
- Henok Biadglign Ademtew ,
- Hernán Maina ,
- Holy Lovenia ,
- Israel Abebe Azime ,
- Jan Christian Blaise Cruz ,
- Jay Gala ,
- Jiahui Geng ,
- Jesús-Germán Ortiz-Barajas ,
- Jinheon Baek ,
- Jocelyn Dunstan ,
- L. A. Alemany ,
- Kumaranage Ravindu Yasas Nagasinghe ,
- Luciana Benotti ,
- L. F. D’Haro ,
- Marcelo Viridiano ,
- Marcos Estecha-Garitagoitia ,
- Maria Camila Buitrago Cabrera ,
- Mario Rodr'iguez-Cantelar ,
- Mélanie Jouitteau ,
- M. Mihaylov ,
- Mohamed Fazli Mohamed Imam ,
- Muhammad Farid Adilazuarda ,
- Munkhjargal Gochoo ,
- Munkh-Erdene Otgonbold ,
- Naome A. Etori ,
- Olivier Niyomugisha ,
- Paula M'onica Silva ,
- Pranjal A. Chitale ,
- Raj Dabre ,
- Rendi Chevi ,
- Ruochen Zhang ,
- Ryandito Diandaru ,
- Samuel Cahyawijaya ,
- Santiago G'ongora ,
- Soyeong Jeong ,
- Sukannya Purkayastha ,
- Tatsuki Kuribayashi ,
- Thanmay Jayakumar ,
- T. Torrent ,
- Toqeer Ehsan ,
- Vladimir Araujo ,
- Yova Kementchedjhieva ,
- Zara Burzo ,
- Zheng Wei Lim ,
- Zheng-Xin Yong ,
- Oana Ignat ,
- Joan Nwatu ,
- Rada Mihalcea ,
- T. Solorio ,
- Alham Fikri Aji
NeurIPS 2024 |
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.