Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
106
Modeling and Similitude01:12

Modeling and Similitude

261
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
261
Nonconscious Mimicry01:13

Nonconscious Mimicry

4.5K
Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
4.5K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

AttentionMNIST: a mouse-click attention tracking dataset for handwritten numeral and alphabet recognition.

Scientific reports·2023
查看所有相关文章

相关实验视频

Updated: Jun 22, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.7K

基于注意力的变化自编码模型,用于通过世代识别人与人之间的交互.

Bonny Banerjee1, Murchana Baruah1

  • 1Institute for Intelligent Systems, and Department of Electrical & Computer Engineering, University of Memphis, Memphis, TN 38152, USA.

Sensors (Basel, Switzerland)
|June 27, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了新的多式联络代理模型,用于从3D骨数据中预测人类意图. 这些模型提供与最先进的方法具有较少参数的可比精度,推进人工智能应用.

关键词:
关注注意力注意力注意力注意力具体化的人工智能代理人人与人之间的人际交互的产生人类人类互动识别识别预测的意图预测的预测.长期和短期记忆 (LSTM)这是一个多式联络模式.感知 感知 感知 感知自己的感觉 (proprioception) 是一种感觉.经常性神经网络 (RNN)变量自动编码器变量自动编码器

更多相关视频

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

17.3K
A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.6K

相关实验视频

Last Updated: Jun 22, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.7K
Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

17.3K
A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.6K

科学领域:

  • 人工智能的人工智能
  • 机器人技术 机器人技术 机器人技术
  • 计算机视觉 计算机视觉

背景情况:

  • 人类意图预测对于开发至关重要,并具有众多应用.
  • 现有的意图预测模型是有限的.
  • 意图预测的早期发展凸显了它的重要性.

研究的目的:

  • 提出基于注意力的新型代理模型,用于预测3D骨相互作用的意图.
  • 开发具有内在多式模式的模型,具有感知和自身感知途径.
  • 提高意图预测模型的效率和准确性.

主要方法:

  • 利用两种基于注意力的代理模型,通过见取样3D骨.
  • 在代理设计中纳入多式途径 (感知和自身感知).
  • 训练有素的代理人通过学习最佳采样策略来最大限度地减少生成和分类错误.

主要成果:

  • 一个拟议的模型实现了与最先进的技术状态相比的分类和生成准确性.
  • 有效的模型包含的可训练参数比现有方法少.
  • 对基准数据集进行了评估,并与最先进的模型进行了对比.

结论:

  • 拟议的多式联络代理模型显示了准确和高效的意图预测的前景.
  • 这些模型的洞察力可以指导未来人工智能代理商的开发.
  • 这些发现有助于人类机器人交互和自主系统的进步.