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
Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision-Language Models
Keyan Guo, Ayush Utkarsh, Wenbo Ding, Isabelle Ondracek, Ziming Zhao, Guo Freeman, Nishant Vishwamitra, Hongxin Hu
Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding
Xintong Wang, Jingheng Pan, Liang Ding, Chris Biemann
Toward Interactive Regional Understanding in Vision-Large Language Models
Jungbeom Lee, Sanghyuk Chun, Sangdoo Yun
Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond
Tianxin Wei, Bowen Jin, Ruirui Li, Hansi Zeng, Zhengyang Wang, Jianhui Sun, Qingyu Yin, Hanqing Lu, Suhang Wang, Jingrui He, Xianfeng Tang
Mitigating Dialogue Hallucination for Large Vision Language Models via Adversarial Instruction Tuning
Dongmin Park, Zhaofang Qian, Guangxing Han, Ser-Nam Lim
XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization
Yequan Bie, Luyang Luo, Zhixuan Chen, Hao Chen
B-AVIBench: Towards Evaluating the Robustness of Large Vision-Language Model on Black-box Adversarial Visual-Instructions
Hao Zhang, Wenqi Shao, Hong Liu, Yongqiang Ma, Ping Luo, Yu Qiao, Nanning Zheng, Kaipeng Zhang
Griffon v2: Advancing Multimodal Perception with High-Resolution Scaling and Visual-Language Co-Referring
Yufei Zhan, Yousong Zhu, Hongyin Zhao, Fan Yang, Ming Tang, Jinqiao Wang
The First to Know: How Token Distributions Reveal Hidden Knowledge in Large Vision-Language Models?
Qinyu Zhao, Ming Xu, Kartik Gupta, Akshay Asthana, Liang Zheng, Stephen Gould
AIGCs Confuse AI Too: Investigating and Explaining Synthetic Image-induced Hallucinations in Large Vision-Language Models
Yifei Gao, Jiaqi Wang, Zhiyu Lin, Jitao Sang
Efficient Prompt Tuning of Large Vision-Language Model for Fine-Grained Ship Classification
Long Lan, Fengxiang Wang, Xiangtao Zheng, Zengmao Wang, Xinwang Liu