Fine-Grained Post-Training Quantization for Large Vision Language Models with Quantization-Aware Integrated Gradients
细粒度后训练量化用于大型视觉语言模型的量化感知集成梯度
机构 * State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA(多模态人工智能系统国家重点实验室,中国科学院自动化所) ; School of Artificial Intelligence, UCAS(人工智能学院,中国科学院大学) ; Beijing National Research Center for Information Science and Technology(北京信息科学研究中心) ; Institute of Artificial Intelligence, USTB(信息科学技术大学人工智能学院) ; School of Artificial Intelligence, Beihang University(北京航空航天大学人工智能学院) ; Zhongguancun Academy(中关村学院)
AI总结 本文提出细粒度后训练量化方法,通过量化感知集成梯度评估token敏感性,提升大型视觉语言模型的精度与效率。
Comments Accepted by CVPR 2026 Main Conference