arXiv · AI· Samuel Cahyawijaya, Peerat Limkonchotiwat, Tack Hwa Wong, Hitesh Laxmichand Patel, Amit Agarwal, Manuel Antonio Rufino, Carlos Rafael Catalan, Muhammad Reza Qorib, Vicky Feliren, Holy Lovenia, Aye Hninn Khine, Frederikus Hudi, David Anugraha, Alham Fikri Aji, Romrawin Chumpu, Viet-Thanh Pham, Minghan Wang, Mohamed Fazli Imam, Ruochen Zhang, Joseph Marvin Imperial, Khumaisa Nur'aini, Do Xuan Long, Musa Izzanardi Wijanarko, Joel Ruben Antony Moniz, Patrick Amadeus Irawan, Hanif Muhammad Zhafran, Isaiah Flores, Salsabila Zahirah Pranida, Jun Kevin, Jostin Jerico Rosal, Patricia Nicole Monderin, Kun Kerdthaisong, Ahmad Mustafid, My Chiffon Nguyen, Natchapon Jongwiriyanurak, Siva Worajitwannakul, Haochen Li, Adrian Xuan Wei Lim, Bin Wang, Muhammad Ravi Shulthan Habibi, Lynnette Hui Xian Ng, Mithil Bangera, Yeshil Bangera, Priyaranjan Pattnayak, Dun Li Chan, Sherissa Caren Djuniwar, Cho Chan Myei Oo, Hee Ming Shan·· 8 小时前AI 评分26
多模态视觉语言模型中的类人区域适应性框架
Anthropogenic Regional Adaptation in Multimodal Vision-Language Model
AI 导读
研究提出“类人区域适应性”范式及GG-EZ方法，通过区域数据过滤和模型合并优化模型的区域相关性。 在大型视觉语言模型、文生图扩散模型和视觉语言嵌入模型等3种架构上的实验显示，该方法在东南亚地区文化相关性指标上提升5-15%，同时保持98%以上的全球性能。 该研究为多模态视觉语言模型在多样化地区的适用性建立了基础范式。
来源：arXiv · AI · arxiv.org