Anthropogenic Regional Adaptation in Multimodal Vision-Language Model
AI 摘要
论文提出了一种人为区域适应范式，优化多模态模型在特定区域的文化相关性，同时保持全局泛化能力。
主要贡献
- 提出人为区域适应（Anthropogenic Regional Adaptation）范式
- 提出GG-EZ适应方法
- 验证了该方法在东南亚区域适应上的有效性
方法论
使用区域数据过滤和模型合并方法GG-EZ，在VL模型上进行区域适应，并在东南亚进行案例研究。
原文摘要
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.