Large Vision Language Model
Large Vision-Language Models (LVLMs) integrate computer vision and natural language processing to enable machines to understand and reason about images and text simultaneously. Current research focuses on improving LVLMs' accuracy, efficiency, and robustness, particularly addressing issues like hallucinations (generating inaccurate information), and enhancing their ability to perform multi-level visual perception and reasoning tasks, including quantitative spatial reasoning and mechanical understanding. These advancements are significant for various applications, including medical image analysis, robotics, and autonomous driving, by enabling more reliable and insightful multimodal data processing.
Papers
InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model
Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Bin Wang, Linke Ouyang, Xilin Wei, Songyang Zhang, Haodong Duan, Maosong Cao, Wenwei Zhang, Yining Li, Hang Yan, Yang Gao, Xinyue Zhang, Wei Li, Jingwen Li, Kai Chen, Conghui He, Xingcheng Zhang, Yu Qiao, Dahua Lin, Jiaqi Wang
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
Bin Lin, Zhenyu Tang, Yang Ye, Jiaxi Cui, Bin Zhu, Peng Jin, Jinfa Huang, Junwu Zhang, Munan Ning, Li Yuan