Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2503.07920

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·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
View original source ↗Read source document ↗Export metadata ↓

Abstract

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.

Explore related subjects

cs.CVcs.AIcs.CL

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

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. 2025-03-18. Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia. https://arxiv.org/abs/2503.07920

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Lifelong Learning of Video Diffusion Models From a Single Video Stream

Video diffusion models can enable embodied agents to anticipate plausible futures from the recent past, but they are typically trained offline on curated datasets--a mismatch with the agents' learning setup at deployment: online, from a single video stream that sequentially outputs one frame at a time. We bridge this training gap and demonstrate that training autoregressive video diffusion models from such a stream, resembling the experience of embodied agents, is not only possible but can also perform comparably to standard offline training given the same number of gradient steps. We find that this robustness to video stream autocorrelation and nonstationarity can be achieved using experience replay methods that retain a subset of the video stream. To support training and evaluation in this setting, we introduce five new datasets for streaming lifelong generative video modeling: Lifelong Bouncing Balls (O), Lifelong Bouncing Balls (C), Lifelong 3D Maze, Lifelong Drive, and Lifelong PLAICraft, each consisting of one million consecutive frames from environments of increasing complexity. Together, our datasets and experiments lay the groundwork for video generative models and world models that continuously learn from single-sensor video streams rather than fixed datasets.

cs.CV↗
arXiv2026

Bayesian Fusion of Active Contour Models and ConvNet Priors for Standing Dead Tree Segmentation

Instance segmentation is a core computer vision task with great practical significance. Recent advances, driven by large-scale benchmark datasets, have yielded good general-purpose Convolutional Neural Network (CNN)-based methods. Natural Resource Monitoring (NRM) utilizes remote sensing imagery with generally known scale and containing multiple overlapping instances of the same class, wherein the object contours are jagged and highly irregular. This is in stark contrast with the regular man-made objects found in classic benchmark datasets. We address this problem and propose a novel instance segmentation method geared towards NRM imagery. We formulate the problem as Bayesian maximum a posteriori inference which, in learning the individual object contours, incorporates shape, location, and position priors from state-of-the-art CNN architectures, driving a simultaneous level-set evolution of multiple object contours. We employ loose coupling between the CNNs that supply the priors and the active contour process, allowing a drop-in replacement of new network architectures. Moreover, we introduce a novel prior for contour shape, namely, a class of Deep Shape Models based on architectures from Generative Adversarial Networks (GANs). These Deep Shape Models are in essence a non-linear generalization of the classic Eigenshape formulation. In experiments, we tackle the challenging, real-world problem of segmenting individual dead tree crowns and delineating precise contours. We compare our method to two leading general-purpose instance segmentation methods - Mask R-CNN and K-net - on color infrared aerial imagery. Results show our approach to significantly outperform both methods in terms of reconstruction quality of tree crown contours. Furthermore, use of the GAN-based deep shape model prior yields significant improvement of all results over the vanilla Eigenshape prior.

cs.CV↗
arXiv2026

Sonicmesh: Enhancing 3D Human Mesh Reconstruction in Vision-Impaired Environments With Acoustic Signals

3D human mesh reconstruction (HMR) from RGB images often degrades under poor illumination, occlusion, and non-line-of-sight conditions. Acoustic sensing provides complementary spatial cues but suffers from low spatial resolution. We propose SonicMesh, which, to the best of our knowledge, is the first acoustic--visual framework for robust 3D human mesh reconstruction. SonicMesh first converts ultrasonic echoes into range--azimuth acoustic images through an Inverse Synthetic Aperture Radar (ISAR)-based imaging process. It then introduces a cross-dimensional anatomical registration module that maps modality-specific 2D joint features into a common canonical 3D human space. The registered anatomical representations are further integrated with acoustic and visual features through a two-stage fusion network for final mesh reconstruction. Experiments demonstrate that SonicMesh achieves accurate and robust 3D human reconstruction across normal, poor-light, occluded, and non-line-of-sight environments, consistently outperforming existing RGB-, radio-frequency (RF)-, and mmWave-based approaches under challenging sensing conditions.

cs.CV↗