arXiv 2503.079202025-03-18cs.CVcs.AIcs.CL

众包、爬取还是生成？构建SEA-VL：一个面向东南亚的多文化视觉-语言数据集

Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia

Samuel Cahyawijaya, Holy Lovenia, Joel Ruben Antony Moniz, Tack Hwa Wong, Mohammad Rifqi Farhansyah, Thant Thiri Maung, Frederikus Hudi, David Anugraha, Muhamma… 展开作者

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 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, 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 Flores, Kenneth Ko Han Chen, Anjela Gail 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 V. 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

备注：: [SEA-VL Dataset] https://huggingface.co/collections/SEACrowd/sea-vl-multicultural-vl-dataset-for-southeast-asia-67cf223d0c341d4ba2b236e7 [Appendix J] https://github.com/SEACrowd/seacrowd.github.io/blob/master/docs/SEA_VL_Appendix_J.pdf

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AI总结：

针对东南亚在视觉-语言研究中代表性不足的问题，研究团队构建了开源多文化数据集SEA-VL，探索众包、爬取、生成三种图像收集方式，最终收集128万张文化相关图像，助力打造更具包容性的AI系统。

摘要：

东南亚（SEA）是一个语言和文化极具多样性的地区，但它在视觉-语言（VL）研究中的代表性仍然严重不足，这往往导致人工智能（AI）模型无法捕捉东南亚的文化细微差别。为填补这一空白，我们提出了SEA-VL，这是一个致力于为东南亚语言开发高质量、具备文化相关性数据的开源项目。通过邀请来自东南亚国家的贡献者参与，SEA-VL旨在确保更好的文化相关性与多样性，推动代表性不足的语言更多地融入VL研究。除众包外，我们的项目还进一步探索了通过爬取和图像生成自动收集文化相关图像的方式。首先，我们发现图像爬取可达到约85%的文化相关性，且比众包更具成本和时间效率。其次，尽管生成式视觉模型已取得 substantial 进展，但合成图像在准确反映东南亚文化方面仍不可靠，生成的图像往往无法体现该地区细致的传统和文化语境。我们总共收集了128万张东南亚文化相关图像，规模是其他现有数据集的50倍以上。通过SEA-VL，我们旨在弥合东南亚的代表性差距，推动开发更具包容性、能真实呈现东南亚多元文化的AI系统。

英文摘要：

Southeast Asia (SEA) is a region of extraordinary linguistic and cultural diversity, yet it remains significantly underrepresented in vision-language (VL) research. This often results in artificial intelligence (AI) models that fail to capture SEA cultural nuances. To fill this gap, we present SEA-VL, an open-source initiative dedicated to developing high-quality, culturally relevant data for SEA languages. By involving contributors from SEA countries, SEA-VL aims to ensure better cultural relevance and diversity, fostering greater inclusivity of underrepresented languages in VL research. Beyond crowdsourcing, our initiative goes one step further in the exploration of the automatic collection of culturally relevant images through crawling and image generation. First, we find that image crawling achieves approximately ~85% cultural relevance while being more cost- and time-efficient than crowdsourcing. Second, despite the substantial progress in generative vision models, synthetic images remain unreliable in accurately reflecting SEA cultures. The generated images often fail to reflect the nuanced traditions and cultural contexts of the region. Collectively, we gather 1.28M SEA culturally-relevant images, more than 50 times larger than other existing datasets. Through SEA-VL, we aim to bridge the representation gap in SEA, fostering the development of more inclusive AI systems that authentically represent diverse cultures across SEA.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。