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
From Pixels to Prose: A Large Dataset of Dense Image Captions
Vasu Singla, Kaiyu Yue, Sukriti Paul, Reza Shirkavand, Mayuka Jayawardhana, Alireza Ganjdanesh, Heng Huang, Abhinav Bhatele, Gowthami Somepalli, Tom Goldstein
VEGA: Learning Interleaved Image-Text Comprehension in Vision-Language Large Models
Chenyu Zhou, Mengdan Zhang, Peixian Chen, Chaoyou Fu, Yunhang Shen, Xiawu Zheng, Xing Sun, Rongrong Ji
Detecting and Evaluating Medical Hallucinations in Large Vision Language Models
Jiawei Chen, Dingkang Yang, Tong Wu, Yue Jiang, Xiaolu Hou, Mingcheng Li, Shunli Wang, Dongling Xiao, Ke Li, Lihua Zhang
Creating a Lens of Chinese Culture: A Multimodal Dataset for Chinese Pun Rebus Art Understanding
Tuo Zhang, Tiantian Feng, Yibin Ni, Mengqin Cao, Ruying Liu, Katharine Butler, Yanjun Weng, Mi Zhang, Shrikanth S. Narayanan, Salman Avestimehr
AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models
Yuhang Wu, Wenmeng Yu, Yean Cheng, Yan Wang, Xiaohan Zhang, Jiazheng Xu, Ming Ding, Yuxiao Dong
INS-MMBench: A Comprehensive Benchmark for Evaluating LVLMs' Performance in Insurance
Chenwei Lin, Hanjia Lyu, Xian Xu, Jiebo Luo
MMFakeBench: A Mixed-Source Multimodal Misinformation Detection Benchmark for LVLMs
Xuannan Liu, Zekun Li, Peipei Li, Shuhan Xia, Xing Cui, Linzhi Huang, Huaibo Huang, Weihong Deng, Zhaofeng He
VLind-Bench: Measuring Language Priors in Large Vision-Language Models
Kang-il Lee, Minbeom Kim, Seunghyun Yoon, Minsung Kim, Dongryeol Lee, Hyukhun Koh, Kyomin Jung
DiffuSyn Bench: Evaluating Vision-Language Models on Real-World Complexities with Diffusion-Generated Synthetic Benchmarks
Haokun Zhou, Yipeng Hong
Jailbreak Vision Language Models via Bi-Modal Adversarial Prompt
Zonghao Ying, Aishan Liu, Tianyuan Zhang, Zhengmin Yu, Siyuan Liang, Xianglong Liu, Dacheng Tao
Uncovering Bias in Large Vision-Language Models at Scale with Counterfactuals
Phillip Howard, Kathleen C. Fraser, Anahita Bhiwandiwalla, Svetlana Kiritchenko
Enhancing Large Vision Language Models with Self-Training on Image Comprehension
Yihe Deng, Pan Lu, Fan Yin, Ziniu Hu, Sheng Shen, James Zou, Kai-Wei Chang, Wei Wang