Anthropogenic Regional Adaptation in Multimodal Vision-Language Model
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.
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Samuel Cahyawijaya
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Peerat Limkonchotiwat
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Tack Hwa Wong
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Hitesh Laxmichand Patel
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Amit Agarwal
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Manuel Antonio Rufino
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Carlos Rafael Catalan
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Muhammad Reza Qorib
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Vicky Feliren
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Holy Lovenia
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Aye Hninn Khine
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Frederikus Hudi
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David Anugraha
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Alham Fikri Aji
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Romrawin Chumpu
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Viet-Thanh Pham
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Minghan Wang
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Mohamed Fazli Imam
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Ruochen Zhang
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Joseph Marvin Imperial
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Do Xuan Long
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Musa Izzanardi Wijanarko
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Joel Ruben Antony Moniz
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Patrick Amadeus Irawan
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Hanif Muhammad Zhafran
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Isaiah Flores
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Ira Salsabila
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Jun Kevin
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Jostin Jerico Rosal
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Patricia Nicole Monderin
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Kun Kerdthaisong
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Ahmad Mustafid
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My Chiffon Nguyen
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Natchapon Jongwiriyanurak
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Siva Worajitwannakul
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Haochen Li
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Adrian Xuan Wei Lim
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Bin Wang
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Muhammad Ravi Shulthan Habibi
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Lynnette Hui Xian Ng
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Mithil Bangera
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Yeshil Bangera
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Priyaranjan Pattnayak
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Dun Li Chan
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Sherissa Caren Djuniwar
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Hee Ming Shan
Anthropogenic Regional Adaptation in Multimodal Vision-Language Model
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.
Vision-language models can be adapted for regional contexts through Anthropogenic Regional Adaptation and GG-EZ method while maintaining global performance and improving cultural relevance.
vision-language models regional adaptation cultural relevance global generalization Anthropogenic Regional Adaptation Geographical-generalization-made-easy model merging regional data filtering