情景感知阶段:Planner 依据子任务和与当前子任务最相关的环境信息以及从记忆中搜寻和当前场景相关的成功规划方案,来生成最符合当前情景的动作序列。 具身行动阶段:Perfomer 执行 Planner 生成的动作序列,并每隔一定的时间间隔询问 Patroller 以获取当前环境信息判断是否需要切换下一个动作。 重规划和记忆阶段:在整个运行过程...
我们以“multi-modal”为关键词检索了已接受论文列表，CVPR2024总计有78篇相关论文，相比于CVPR2023 中多...
CVPR 2024 Under Review | Less is More:A Closer Look at Multi-Modal Few-Shot Learning 楼下小黑 今朝有酒今朝醉,明天没酒明天睡2 人赞同了该文章 来自浙大的一篇文章,看模板应该是投稿CVPR,主要关注的问题是预训练模型中如何充分利用few-shot的能力,主要的方法是利用zero-shot能力和learnable prompt,使用self...
同时由于水印和图像语义高度耦合,高斯底纹还表现出很强的鲁棒性。 OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation (CVPR 2024) 作者:黄启栋(中国科学技术大学),董潇逸(香港中文大学),...
OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation 论文地址: https://arxiv.org/abs/2311.17911 代码地址: https://github.com/shikiw/OPERA 1、背景 从LLaVA 到 Qwen-VL,从 GPT...
[1] Shenghai Yuan, Yizhuo Yang, Thien Hoang Nguyen, Thien-Minh Nguyen, Jianfei Yang, Fen Liu, Jianping Li, Han Wang, Lihua Xie. MMAUD: A Comprehensive Multi-Modal Anti-UAV Dataset for Modern Miniature Drone Threats. In 2023 IEEE International Conference on Robotics and Automation (ICRA). ...
OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation 该论文针对多模态LLM的幻觉问题,提出了过度信任惩罚和回顾分配机制。项目代码:https://github.com/shikiw/OPERA Making Large Multimodal Models Understand Arbitrary Visual Prompts ...
人类：面部，身体，姿势，手势 Suppress and Rebalance: Towards Generalized Multi-Modal Face Anti-...
mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration Paper:https://arxiv.org/abs/2311.04257 Code:https://github.com/X-PLUG/mPLUG-Owl/tree/main/mPLUG-Owl2 Model:https://www.modelscope.cn/models/iic/mPLUG-Owl2/summary(可下载模型权重) ...
76、MedM2G: Unifying Medical Multi-Modal Generation via Cross-Guided Diffusion with Visual Invariant 二十五、交通驾驶 77、Controllable Safety-Critical Closed-loop Traffic Simulation via Guided Diffusion https://safe-sim.github.io/ 78、Generalized Predictive Model for Autonomous Driving ...