Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia
Authors: Samuel Cahyawijaya; Holy Lovenia; Joel Ruben Antony Moniz; Tack Hwa Wong; Mohammad Rifqi Farhansyah; Thant Thiri Maung; Frederikus Hudi; David Anugraha; Muhammad Ravi Shulthan Habibi; Muhammad Reza Qorib; Amit Agarwal; Joseph Marvin Imperial; Hitesh Laxmichand Patel; Vicky Feliren; Bahrul Ilmi Nasution; Manuel Antonio Rufino; Genta Indra Winata; Rian Adam Rajagede; Carlos Rafael Catalan; Mohamed Fazli Mohamed Imam; Priyaranjan Pattnayak; Salsabila Zahirah Pranida; Kevin Pratama; Yeshil Bangera; Adisai Na-Thalang; Patricia Nicole Monderin; Yueqi Song; Christian Simon; Lynnette Hui Xian Ng; Richardy Lobo Sapan; Taki Hasan Rafi; Bin Wang; Supryadi; Kanyakorn Veerakanjana; Piyalitt Ittichaiwong; Matthew Theodore Roque; Karissa Vincentio; Takdanai Kreangphet; Phakphum Artkaew; Kadek Hendrawan Palgunadi; Yanzhi Yu; Rochana Prih Hastuti; William Nixon; Mithil Bangera; Adrian Xuan Wei Lim; Aye Hninn Khine; Hanif Muhammad Zhafran; Teddy Ferdinan; Audra Aurora Izzani; Ayushman Singh; Evan Evan; Jauza Akbar Krito; Michael Anugraha; Fenal Ashokbhai Ilasariya; Haochen Li; John Amadeo Daniswara; Filbert Aurelian Tjiaranata; Eryawan Presma Yulianrifat; Can Udomcharoenchaikit; Fadil Risdian Ansori; Mahardika Krisna Ihsani; Giang Nguyen; Anab Maulana Barik; Dan John Velasco; Rifo Ahmad Genadi; Saptarshi Saha; Chengwei Wei; Isaiah Edri W. Flores; Kenneth Chen Ko Han; Anjela Gail D. Santos; Wan Shen Lim; Kaung Si Phyo; Tim Santos; Meisyarah Dwiastuti; Jiayun Luo; Jan Christian Blaise Cruz; Ming Shan Hee; Ikhlasul Akmal Hanif; M.Alif Al Hakim; Muhammad Rizky Sya’ban; Kun Kerdthaisong; Lester James Validad Miranda; Fajri Koto; Tirana Noor Fatyanosa; Alham Fikri Aji; Jostin Jerico Rosal; Jun Kevin; Robert Wijaya; Onno P. Kampman; Ruochen Zhang; Börje F. Karlsson; Peerat Limkonchotiwat
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Despite Southeast Asia’s (SEA) extraordinary linguistic and cultural diversity, the region remains significantly underrepresented in vision-language (VL) research, resulting in AI models that inadequately capture SEA cultural nuances. To fill this gap, we present SEA-VL, an open-source initiative dedicated to developing culturally relevant high-quality datasets for SEA languages. By involving contributors from SEA countries, SEA-VL ensures better cultural relevance and diversity, fostering greater inclusivity of underrepresented languages and cultural depictions in VL research. Our methodology employed three approaches: community-driven crowdsourcing with SEA contributors, automated image crawling, and synthetic image generation. We evaluated each method’s effectiveness in capturing cultural relevance. We found that image crawling achieves approximately ~85% cultural relevance while being more cost- and time-efficient than crowdsourcing, whereas synthetic image generation failed to accurately reflect SEA cultural nuances and contexts. Collectively, we gathered 1.28 million SEA culturally relevant images, more than 50 times larger than other existing datasets. This work bridges the representation gap in SEA, establishes a foundation for developing culturally aware AI systems for this region, and provides a replicable framework for addressing representation gaps in other underrepresented regions.