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

Naturalistic Observations02:30

Naturalistic Observations

15.5K
If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
15.5K

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

Burst-firing and extreme multistability in a dual-neuron fractional-order memristive HNN with hardware implementation.

Chaos (Woodbury, N.Y.)·2025
Same author

A class of n-D Hamiltonian conservative chaotic systems with three-terminal memristor: Modeling, dynamical analysis, and FPGA implementation.

Chaos (Woodbury, N.Y.)·2025
Same author

Electromagnetic radiation control for nonlinear dynamics of Hopfield neural networks.

Chaos (Woodbury, N.Y.)·2024
Same author

Dynamic Analysis and FPGA Implementation of a New Fractional-Order Hopfield Neural Network System under Electromagnetic Radiation.

Biomimetics (Basel, Switzerland)·2023
Same author

Dynamics study on the effect of memristive autapse distribution on Hopfield neural network.

Chaos (Woodbury, N.Y.)·2022
Same author

Asymptotic Synchronization of Memristive Cohen-Grossberg Neural Networks with Time-Varying Delays via Event-Triggered Control Scheme.

Micromachines·2022

相关实验视频

Updated: Jul 27, 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

3.9K

MSTA-SlowFast:用于课堂环境的学生行为探测器

Shiwen Zhang1, Hong Liu1, Cheng Sun2

  • 1College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China.

Sensors (Basel, Switzerland)
|June 10, 2023
PubMed
概括

这项研究介绍了一种改进的SlowFast模型,用于从视频中检测学生的课堂行为. 新的MSTA-SlowFast模型提高了检测准确性,有助于指令评估和质量改进.

关键词:
缓慢快速的模型注意力机制注意力机制行为检测 行为检测在课堂上行为检测,行为检测.

更多相关视频

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

7.7K
Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking
05:58

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

Published on: August 29, 2018

8.9K

相关实验视频

Last Updated: Jul 27, 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

3.9K
A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

7.7K
Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking
05:58

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

Published on: August 29, 2018

8.9K

科学领域:

  • 教育技术的教育技术
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 有效地检测学生的课堂行为对于教学评估和提高教学质量至关重要.
  • 分析学生的学习状态可以通过基于视频的自动行为分析来增强.

研究的目的:

  • 提出一个改进的课堂行为检测模型,以提高从教学视频分析学生行为的准确性.
  • 增强多层次的时空信息的提取,并专注于显著的时间特征,以识别行为.

主要方法:

  • 这项研究提出了一种改进的SlowFast模型,该模型包含一个多尺度时空注意 (MSTA) 模块.
  • 引入了有效的时间注意力 (ETA),以改进时间特征的焦点.
  • 一个新的面向时空空间的学生课堂行为数据集被构建用于评估.

主要成果:

  • 与原来的SlowFast模型相比,MSTA-SlowFast模型表现出更高的性能.
  • 在定制数据集上,平均平均精度 (mAP) 提高了5.63%.
  • 改进的模型有效地提取了多个尺度的时空和突出的时间特征.

结论:

  • 拟议的MSTA-SlowFast模型显著改善了从视频中检测学生的课堂行为.
  • 这种进步为教学评估和教学质量提升提供了更好的工具.
  • 开发的数据集和模型为教育视频分析领域做出了贡献.