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

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

SpikeSift: a computationally efficient and drift-resilient spike sorting algorithm.

Journal of neural engineering·2025
Same author

Helios.TALK: A decentralised messaging framework that preserves the privacy of users.

Open research Europe·2025
Same author

Cancer Patients' Perspectives and Requirements of Digital Health Technologies: A Scoping Literature Review.

Cancers·2024
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jun 13, 2025

In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses
07:26

In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses

Published on: December 5, 2019

7.9K

PolyMeme:微粒度的互联网模因感应.

Vasileios Arailopoulos1, Christos Koutlis2, Symeon Papadopoulos2

  • 1School of Electrical & Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.

Sensors (Basel, Switzerland)
|September 14, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了PolyMeme,这是一个用于自动检测互联网meme的多样化数据集. 新的数据集和深度学习模型在识别meme内容方面实现了高精度,有助于跟踪有害的在线信息.

关键词:
迷思的分类 迷思的分类模因检测 模因检测 模因检测模因分类学 模因分类学

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
Ratiometric Calcium Imaging of Individual Neurons in Behaving Caenorhabditis Elegans
11:26

Ratiometric Calcium Imaging of Individual Neurons in Behaving Caenorhabditis Elegans

Published on: February 7, 2018

11.6K

相关实验视频

Last Updated: Jun 13, 2025

In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses
07:26

In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses

Published on: December 5, 2019

7.9K
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
Ratiometric Calcium Imaging of Individual Neurons in Behaving Caenorhabditis Elegans
11:26

Ratiometric Calcium Imaging of Individual Neurons in Behaving Caenorhabditis Elegans

Published on: February 7, 2018

11.6K

科学领域:

  • 计算机科学 计算机科学
  • 社交媒体分析 社交媒体分析
  • 人工智能的人工智能

背景情况:

  • 互联网meme是一种在社交媒体上流行的多式联络数字内容格式.
  • 自动meme检测对于跟踪趋势和有害内容传播至关重要.
  • 现有的数据集在meme格式,风格和内容方面缺乏多样性.

研究的目的:

  • 介绍一下PolyMeme数据集,这是一个多样化的集合,包含四个类别的约2.7万个meme.
  • 为了解决现有数据集在捕获meme种类中的局限性.
  • 开发和评估深度学习模型,以准确检测模因.

主要方法:

  • 从Reddit收集了大约27000个meme,并对它们进行了分类.
  • 手动标记数据集的一部分用于培训和验证.
  • 训练有素的深度学习网络 (ResNet,ViT) 使用PolyMeme数据集和其他图像数据集进行模因检测.

主要成果:

  • 在PolyMeme上训练的深度学习模型在分类方面估计达到了7.35%的错误率.
  • 模因检测模型的准确性很高,在测试组中达到98%.
  • 包含正规图像和文本并没有显著改善meme检测性能.

结论:

  • PolyMeme数据集通过考虑各种meme格式来增强meme检测能力.
  • 通过先进的深度学习技术和全面的数据集,精确的自动meme检测是可行的.
  • 这项工作有助于更好地理解和缓解在线错误信息和通过meme传播有害内容的传播.