Vision Model
Vision models are artificial intelligence systems designed to interpret and understand visual information, aiming to replicate aspects of human visual perception and reasoning. Current research emphasizes improving efficiency and generalization across diverse tasks, focusing on architectures like Vision Transformers and Convolutional Neural Networks, often incorporating large language models for multimodal understanding and instruction following. This field is crucial for advancing various applications, from medical image analysis and robotic manipulation to enhancing accessibility and creative tools, with ongoing efforts to improve model robustness, explainability, and alignment with human perception.
Papers
Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models
Yushi Hu, Otilia Stretcu, Chun-Ta Lu, Krishnamurthy Viswanathan, Kenji Hata, Enming Luo, Ranjay Krishna, Ariel Fuxman
Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning
Zhuo Huang, Chang Liu, Yinpeng Dong, Hang Su, Shibao Zheng, Tongliang Liu
Semantic-Aware Frame-Event Fusion based Pattern Recognition via Large Vision-Language Models
Dong Li, Jiandong Jin, Yuhao Zhang, Yanlin Zhong, Yaoyang Wu, Lan Chen, Xiao Wang, Bin Luo
LLVMs4Protest: Harnessing the Power of Large Language and Vision Models for Deciphering Protests in the News
Yongjun Zhang
VALUED -- Vision and Logical Understanding Evaluation Dataset
Soumadeep Saha, Saptarshi Saha, Utpal Garain
A Survey on Multimodal Large Language Models for Autonomous Driving
Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Yang Zhou, Kaizhao Liang, Jintai Chen, Juanwu Lu, Zichong Yang, Kuei-Da Liao, Tianren Gao, Erlong Li, Kun Tang, Zhipeng Cao, Tong Zhou, Ao Liu, Xinrui Yan, Shuqi Mei, Jianguo Cao, Ziran Wang, Chao Zheng