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
Split to Merge: Unifying Separated Modalities for Unsupervised Domain Adaptation
Xinyao Li, Yuke Li, Zhekai Du, Fengling Li, Ke Lu, Jingjing Li
An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models
Liang Chen, Haozhe Zhao, Tianyu Liu, Shuai Bai, Junyang Lin, Chang Zhou, Baobao Chang
Can LLMs' Tuning Methods Work in Medical Multimodal Domain?
Jiawei Chen, Yue Jiang, Dingkang Yang, Mingcheng Li, Jinjie Wei, Ziyun Qian, Lihua Zhang
HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding
Zhaorun Chen, Zhuokai Zhao, Hongyin Luo, Huaxiu Yao, Bo Li, Jiawei Zhou
Multimodal ArXiv: A Dataset for Improving Scientific Comprehension of Large Vision-Language Models
Lei Li, Yuqi Wang, Runxin Xu, Peiyi Wang, Xiachong Feng, Lingpeng Kong, Qi Liu
IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding
Lanyun Zhu, Deyi Ji, Tianrun Chen, Peng Xu, Jieping Ye, Jun Liu
A Cognitive Evaluation Benchmark of Image Reasoning and Description for Large Vision-Language Models
Xiujie Song, Mengyue Wu, Kenny Q. Zhu, Chunhao Zhang, Yanyi Chen
Representing Online Handwriting for Recognition in Large Vision-Language Models
Anastasiia Fadeeva, Philippe Schlattner, Andrii Maksai, Mark Collier, Efi Kokiopoulou, Jesse Berent, Claudiu Musat
Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding
Ailin Deng, Zhirui Chen, Bryan Hooi