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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.07920 |
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| _version_ | 1866910881667874816 |
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| author | Cahyawijaya, Samuel Lovenia, Holy Moniz, Joel Ruben Antony Wong, Tack Hwa Farhansyah, Mohammad Rifqi Maung, Thant Thiri Hudi, Frederikus Anugraha, David Habibi, Muhammad Ravi Shulthan Qorib, Muhammad Reza Agarwal, Amit Imperial, Joseph Marvin Patel, Hitesh Laxmichand Feliren, Vicky Nasution, Bahrul Ilmi Rufino, Manuel Antonio Winata, Genta Indra Rajagede, Rian Adam Catalan, Carlos Rafael Imam, Mohamed Fazli Pattnayak, Priyaranjan Pranida, Salsabila Zahirah Pratama, Kevin Bangera, Yeshil Na-Thalang, Adisai Monderin, Patricia Nicole Song, Yueqi Simon, Christian Ng, Lynnette Hui Xian Sapan, Richardy Lobo' Rafi, Taki Hasan Wang, Bin Supryadi Veerakanjana, Kanyakorn Ittichaiwong, Piyalitt Roque, Matthew Theodore Vincentio, Karissa Kreangphet, Takdanai Artkaew, Phakphum Palgunadi, Kadek Hendrawan Yu, Yanzhi Hastuti, Rochana Prih Nixon, William Bangera, Mithil Lim, Adrian Xuan Wei Khine, Aye Hninn Zhafran, Hanif Muhammad Ferdinan, Teddy Izzani, Audra Aurora Singh, Ayushman Evan Krito, Jauza Akbar Anugraha, Michael Ilasariya, Fenal Ashokbhai Li, Haochen Daniswara, John Amadeo Tjiaranata, Filbert Aurelian Yulianrifat, Eryawan Presma Udomcharoenchaikit, Can Ansori, Fadil Risdian Ihsani, Mahardika Krisna Nguyen, Giang Barik, Anab Maulana Velasco, Dan John Genadi, Rifo Ahmad Saha, Saptarshi Wei, Chengwei Flores, Isaiah Chen, Kenneth Ko Han Santos, Anjela Gail Lim, Wan Shen Phyo, Kaung Si Santos, Tim Dwiastuti, Meisyarah Luo, Jiayun Cruz, Jan Christian Blaise Hee, Ming Shan Hanif, Ikhlasul Akmal Hakim, M. Alif Al Sya'ban, Muhammad Rizky Kerdthaisong, Kun Miranda, Lester James V. Koto, Fajri Fatyanosa, Tirana Noor Aji, Alham Fikri Rosal, Jostin Jerico Kevin, Jun Wijaya, Robert Kampman, Onno P. Zhang, Ruochen Karlsson, Börje F. Limkonchotiwat, Peerat |
| author_facet | Cahyawijaya, Samuel Lovenia, Holy Moniz, Joel Ruben Antony Wong, Tack Hwa Farhansyah, Mohammad Rifqi Maung, Thant Thiri Hudi, Frederikus Anugraha, David Habibi, Muhammad Ravi Shulthan Qorib, Muhammad Reza Agarwal, Amit Imperial, Joseph Marvin Patel, Hitesh Laxmichand Feliren, Vicky Nasution, Bahrul Ilmi Rufino, Manuel Antonio Winata, Genta Indra Rajagede, Rian Adam Catalan, Carlos Rafael Imam, Mohamed Fazli Pattnayak, Priyaranjan Pranida, Salsabila Zahirah Pratama, Kevin Bangera, Yeshil Na-Thalang, Adisai Monderin, Patricia Nicole Song, Yueqi Simon, Christian Ng, Lynnette Hui Xian Sapan, Richardy Lobo' Rafi, Taki Hasan Wang, Bin Supryadi Veerakanjana, Kanyakorn Ittichaiwong, Piyalitt Roque, Matthew Theodore Vincentio, Karissa Kreangphet, Takdanai Artkaew, Phakphum Palgunadi, Kadek Hendrawan Yu, Yanzhi Hastuti, Rochana Prih Nixon, William Bangera, Mithil Lim, Adrian Xuan Wei Khine, Aye Hninn Zhafran, Hanif Muhammad Ferdinan, Teddy Izzani, Audra Aurora Singh, Ayushman Evan Krito, Jauza Akbar Anugraha, Michael Ilasariya, Fenal Ashokbhai Li, Haochen Daniswara, John Amadeo Tjiaranata, Filbert Aurelian Yulianrifat, Eryawan Presma Udomcharoenchaikit, Can Ansori, Fadil Risdian Ihsani, Mahardika Krisna Nguyen, Giang Barik, Anab Maulana Velasco, Dan John Genadi, Rifo Ahmad Saha, Saptarshi Wei, Chengwei Flores, Isaiah Chen, Kenneth Ko Han Santos, Anjela Gail Lim, Wan Shen Phyo, Kaung Si Santos, Tim Dwiastuti, Meisyarah Luo, Jiayun Cruz, Jan Christian Blaise Hee, Ming Shan Hanif, Ikhlasul Akmal Hakim, M. Alif Al Sya'ban, Muhammad Rizky Kerdthaisong, Kun Miranda, Lester James V. Koto, Fajri Fatyanosa, Tirana Noor Aji, Alham Fikri Rosal, Jostin Jerico Kevin, Jun Wijaya, Robert Kampman, Onno P. Zhang, Ruochen Karlsson, Börje F. Limkonchotiwat, Peerat |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07920 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia Cahyawijaya, Samuel Lovenia, Holy Moniz, Joel Ruben Antony Wong, Tack Hwa Farhansyah, Mohammad Rifqi Maung, Thant Thiri Hudi, Frederikus Anugraha, David Habibi, Muhammad Ravi Shulthan Qorib, Muhammad Reza Agarwal, Amit Imperial, Joseph Marvin Patel, Hitesh Laxmichand Feliren, Vicky Nasution, Bahrul Ilmi Rufino, Manuel Antonio Winata, Genta Indra Rajagede, Rian Adam Catalan, Carlos Rafael Imam, Mohamed Fazli Pattnayak, Priyaranjan Pranida, Salsabila Zahirah Pratama, Kevin Bangera, Yeshil Na-Thalang, Adisai Monderin, Patricia Nicole Song, Yueqi Simon, Christian Ng, Lynnette Hui Xian Sapan, Richardy Lobo' Rafi, Taki Hasan Wang, Bin Supryadi Veerakanjana, Kanyakorn Ittichaiwong, Piyalitt Roque, Matthew Theodore Vincentio, Karissa Kreangphet, Takdanai Artkaew, Phakphum Palgunadi, Kadek Hendrawan Yu, Yanzhi Hastuti, Rochana Prih Nixon, William Bangera, Mithil Lim, Adrian Xuan Wei Khine, Aye Hninn Zhafran, Hanif Muhammad Ferdinan, Teddy Izzani, Audra Aurora Singh, Ayushman Evan Krito, Jauza Akbar Anugraha, Michael Ilasariya, Fenal Ashokbhai Li, Haochen Daniswara, John Amadeo Tjiaranata, Filbert Aurelian Yulianrifat, Eryawan Presma Udomcharoenchaikit, Can Ansori, Fadil Risdian Ihsani, Mahardika Krisna Nguyen, Giang Barik, Anab Maulana Velasco, Dan John Genadi, Rifo Ahmad Saha, Saptarshi Wei, Chengwei Flores, Isaiah Chen, Kenneth Ko Han Santos, Anjela Gail Lim, Wan Shen Phyo, Kaung Si Santos, Tim Dwiastuti, Meisyarah Luo, Jiayun Cruz, Jan Christian Blaise Hee, Ming Shan Hanif, Ikhlasul Akmal Hakim, M. Alif Al Sya'ban, Muhammad Rizky Kerdthaisong, Kun Miranda, Lester James V. Koto, Fajri Fatyanosa, Tirana Noor Aji, Alham Fikri Rosal, Jostin Jerico Kevin, Jun Wijaya, Robert Kampman, Onno P. Zhang, Ruochen Karlsson, Börje F. Limkonchotiwat, Peerat Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language 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. |
| title | Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2503.07920 |