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

Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

124
Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
124

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

Prevalence and cardiovascular phenotypes of dextrocardia and situs inversus among 277,396 adults: longitudinal evidence of amplified age-related blood pressure progression.

Orphanet journal of rare diseases·2026
Same author

Associations between dairy product and egg intake and NAFLD remission: Evidence from a prospective cohort study.

Nutrition (Burbank, Los Angeles County, Calif.)·2026
Same author

MACTrack: Spatiotemporal context propagation with motion compensation for anti-UAV tracking.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Ternary spike-based neuromorphic signal processing system.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Adaptive Kernel Convolutional Stereo Matching Recurrent Network.

Sensors (Basel, Switzerland)·2024
Same author

An Improved TransMVSNet Algorithm for Three-Dimensional Reconstruction in the Unmanned Aerial Vehicle Remote Sensing Domain.

Sensors (Basel, Switzerland)·2024

相关实验视频

Updated: Jun 4, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.4K

MCCA-VNet:基于面部编码的微表情识别的基于Vit的深度学习方法.

Dehao Zhang1,2, Tao Zhang1,2, Haijiang Sun1

  • 1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了MCCA-VNET,这是一种用于识别微表达式的新型深度学习模型. 它通过考虑空间关系和融合通道和空间注意力机制来提高准确性,优于现有的方法.

关键词:
这就是MCCA-VNET.面部编码 面部编码这是微观表达的微观表达.光学流量方法的光学流量方法.视觉变压器 视觉变压器

更多相关视频

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

2.6K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.3K

相关实验视频

Last Updated: Jun 4, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.4K
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

2.6K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.3K

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 心理学 心理学 心理学

背景情况:

  • 微表达式比宏表达式提供更现实的见解,对心理咨询和临床诊断有价值.
  • 现有的深度学习模型,主要使用光流和变压器,经常忽视面部地标之间的空间关系.

研究的目的:

  • 提出MCCA-VNET,一个深度学习模型,通过结合空间地标关系来增强微表达式识别.
  • 提高微表达式识别的准确性和全面性.

主要方法:

  • 开发了基于变压器架构的MCCA-VNET.
  • 在视觉转换器中集成了道注意力和空间注意力机制.
  • 提取并融合了不断变化的面部特征,强调空间和道相关性.

主要成果:

  • 在一个复合数据集 (SAMM,CAS (ME) II,SMIC) 上,UF1得分为0.8676,UAR得分为0.8622.
  • 与之前的最先进的算法相比,在多个指标上表现出卓越的性能.
  • 通过对基准数据集进行严格的实验测试来验证有效性.

结论:

  • MCCA-VNET显著提高了微表达式识别的准确性.
  • 该模型的注意力机制和空间特征提取的融合提供了一个强大的方法.
  • 提出的方法在微表达式识别任务中实现了最佳的综合性能.