Anthropogenic Regional Adaptation in Multimodal Vision-Language Model
Authors: name: Samuel Cahyawijaya, name: Peerat Limkonchotiwat, name: Tack Hwa Wong, name: Hitesh Laxmichand Patel, name: Amit Agarwal, name: Manuel Antonio Rufino, name: Carlos Rafael Catalan, name: Muhammad Reza Qorib, name: Vicky Feliren, name: Holy Lovenia, name: Aye Hninn Khine, name: Frederikus Hudi, name: David Anugraha, name: Alham Fikri Aji, name: Romrawin Chumpu, name: Viet-Thanh Pham, name: Minghan Wang, name: Mohamed Fazli Imam, name: Ruochen Zhang, name: Joseph Marvin Imperial, name: Do Xuan Long, name: Musa Izzanardi Wijanarko, name: Joel Ruben Antony Moniz, name: Patrick Amadeus Irawan, name: Hanif Muhammad Zhafran, name: Isaiah Flores, name: Ira Salsabila, name: Jun Kevin, name: Jostin Jerico Rosal, name: Patricia Nicole Monderin, name: Kun Kerdthaisong, name: Ahmad Mustafid, name: My Chiffon Nguyen, name: Natchapon Jongwiriyanurak, name: Siva Worajitwannakul, name: Haochen Li, name: Adrian Xuan Wei Lim, name: Bin Wang, name: Muhammad Ravi Shulthan Habibi, name: Lynnette Hui Xian Ng, name: Mithil Bangera, name: Yeshil Bangera, name: Priyaranjan Pattnayak, name: Dun Li Chan, name: Sherissa Caren Djuniwar, name: Hee Ming Shan
Organization: SEACrowd

While the field of vision-language (VL) has achieved remarkable success in integrating visual and textual information across multiple languages and domains, there is still no dedicated framework for assessing human-centric alignment in vision-language systems. We offer two contributions to address this gap. First, we introduce Anthropogenic Regional Adaptation: a novel paradigm that aims to optimize model relevance to specific regional contexts while ensuring the retention of global generalization capabilities. Second, we present a simple, but effective adaptation method named Geographical-generalization-made-easy (GG-EZ), which utilizes regional data filtering and model merging. Through comprehensive experiments on 3 VL architectures: large vision-language models, text-to-image diffusion models, and vision-language embedding models, and a case study in Southeast Asia (SEA) regional adaptation, we demonstrate the importance of Anthropogenic Regional Adaptation and the effectiveness of GG-EZ, showing 5-15% gains in cultural relevance metrics across SEA while maintaining over 98% of global performance and even occasionally surpassing it. Our findings establish Anthropogenic Regional Alignment as a foundational paradigm towards applicability of multimodal vision-language models in diverse regions and demonstrate a simple-yet-effective baseline method that optimizes regional value alignment while preserving global generalization.