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

Association Areas of the Cortex01:21

Association Areas of the Cortex

5.3K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
5.3K
Force Classification01:22

Force Classification

1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.2K
Nonconscious Mimicry01:13

Nonconscious Mimicry

4.6K
Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
4.6K
Reflex Activity01:08

Reflex Activity

1.7K
A reflex activity is an automatic, involuntary response to specific stimuli. It is a part of our survival mechanism, designed to protect us from potential harm. For example, when a bright light suddenly shines into our eyes, we instinctively close them or look away. This is a simple reflex activity orchestrated by the nervous system without conscious thought or effort.
A reflex exam is a diagnostic procedure performed by a healthcare professional to evaluate the functionality of a patient's...
1.7K
Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

153
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...
153

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

Cpeb4 regulates cardiomyocyte apoptosis in heart failure with association to Eif4a2 splicing modulation.

Scientific reports·2026
Same author

Pulpal Pressure Aggravates Pulpitis by Mechano-Inflammatory Signal Synergy.

International dental journal·2026
Same author

The N‑Glycoproteomic Landscape of the Lung in Monocrotaline-Induced Pulmonary Arterial Hypertension.

ACS omega·2026
Same author

<i>RET</i> fusion partners dictate oncogenic potential in undifferentiated spindle cell sarcomas.

Cancer biology & therapy·2026
Same author

ACE2 ameliorates DOX-induced cardiotoxicity by suppressing excessive autophagy via the AMPK/mTOR signaling pathway.

Biochemical pharmacology·2026
Same author

Analysis of the epidemiological features and factors associated with falls among the elderly in urban and rural areas of Chongqing, China: a cross-sectional study.

BMC public health·2026

相关实验视频

Updated: Jul 2, 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

3.8K

跨模态联合的人类活动识别.

Xiaoshan Yang, Baochen Xiong, Yi Huang

    IEEE transactions on pattern analysis and machine intelligence
    |February 20, 2024
    PubMed
    概括

    联合人类活动识别 (FHAR) 通过在设备之间实现协作模型学习来促进隐私. 本研究介绍了跨模式的FHAR (CM-FHAR) 处理各种数据类型,解决更广泛应用的关键挑战.

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 联合人类活动识别 (FHAR) 能够实现活动模型的隐私保护协作学习.
    • 现有的FHAR方法通常假定单模数据,限制了在现实世界场景中的应用,具有不同的本地数据模式.
    • 跨模式联合的人类活动识别 (CM-FHAR) 解决了客户拥有来自不同来源 (例如,运动与视觉) 的数据的场景.

    研究的目的:

    • 引入和解决跨模式联合人类活动识别 (CM-FHAR) 的挑战.
    • 开发一个能够学习全球和私人活动分类器的新型网络 (MCARN),跨越异质数据模式.
    • 克服在联合学习环境中的模式不平衡问题.

    主要方法:

    • 提出模式协作活动识别网络 (MCARN),使用利他主义和自我中心编码器进行模式不可知和模式特定的特征学习.
    • 在特征学习的超球中使用分离损失和对抗模式歧视器.
    • 引入用于模态不平衡的角边际调整方案和关系意识的全球-本地校准机制.

    主要成果:

    • MCARN有效地学习了共享的全球分类器和模式依赖的私有分类器.
    • 提出的方法成功地解决了分布式的共同跨模式特征学习,模式依赖的歧视特征学习和模式不平衡.

    更多相关视频

    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
    Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
    06:49

    Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

    Published on: December 11, 2015

    8.9K

    相关实验视频

    Last Updated: Jul 2, 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

    3.8K
    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
    Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
    06:49

    Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

    Published on: December 11, 2015

    8.9K
  • 在模态平衡和模态不平衡数据集上实现了最先进的性能.
  • 结论:

    • MCARN为跨模式联合的人类活动识别提供了强大的解决方案.
    • 这种方法增强了联合学习的实用性和可扩展性,以识别人类活动.
    • 这项工作为在各种边缘设备上更广泛地部署HAR模型铺平了道路.