Anthropogenic Regional Adaptation in Multimodal Vision-Language Model

TL;DR

This paper introduces Anthropogenic Regional Adaptation and a simple adaptation method GG-EZ to improve regional relevance in vision-language models while maintaining global performance, demonstrated through experiments in Southeast Asia.

Contribution

It proposes a new paradigm for regional adaptation in vision-language models and a straightforward method, GG-EZ, to optimize regional relevance without sacrificing global generalization.

Findings

GG-EZ achieves 5-15% improvement in regional relevance metrics.

Models retain over 98% of their global performance after adaptation.

Regional adaptation enhances cultural relevance in Southeast Asia.

Abstract

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.