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相关概念视频

Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

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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...
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Cognitive Theories: Schachter-Singer Theory of Emotion01:20

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Stanley Schachter and Jerome Singer proposed the two-factor theory of emotion, which emphasizes the interplay between physiological arousal and cognitive labeling in forming emotional experiences. This theory suggests that emotions are not simply a result of physiological responses but rather a combination of these responses and the individual's cognitive interpretation of them.
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Framing Effects03:26

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Information is everywhere and its presentation—such as how and when items are presented—can impact our perceptions and decisions surrounding the info. This broad concept umbrellas framing effects—influences that occur due to the way information is framed in its appearance, whether it’s purely the order or the specific wording of a message. Let’s take a look at numerous ways in which two versions of something can objectively say the same thing, yet we respond in...
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Stereotype Content Model02:16

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Emotional Expression01:26

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Emotional expression encompasses how individuals convey their emotions through verbal communication and non-verbal cues. These non-verbal actions include facial expressions, body language, and physical gestures, such as frowning or smiling. Among these, facial expressions play a crucial role in emotional expression and are understood universally, indicating a biological basis for how humans communicate emotions.
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Persuasion is the process of changing our attitude toward something based on some kind of communication. Much of the persuasion we experience comes from outside forces. How do people convince others to change their attitudes, beliefs, and behaviors? What communications do you receive that attempt to persuade you to change your attitudes, beliefs, and behaviors?
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Updated: Jun 8, 2025

Loneliness Assuaged: Eye-Tracking an Audience Watching Barrage Videos
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在视频广告中解读观众的情绪.

Alexey Antonov1, Shravan Sampath Kumar2, Jiefei Wei3

  • 1WMG, University of Warwick, Coventry, UK.

Scientific reports
|November 2, 2024
PubMed
概括
此摘要是机器生成的。

研究人员开发了一种深度学习模型,用于预测视频中观众的情绪. 通过3万个广告的230多万条注释进行训练,它可以准确地识别情绪反应,帮助内容分析和推.

关键词:
深度学习是一种深度学习.情绪预测 情绪预测视频分析 视频分析

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 情感计算是一种情感计算.

背景情况:

  • 预测视频中的观众情绪对于内容推和广告等应用至关重要.
  • 一个重大挑战是,对于视频,缺乏带有情感注释的大型数据集.
  • 现有的方法受限于数据的可用性和人类情绪反应的复杂性.

研究的目的:

  • 开发和验证一种深度学习模型,用于预测观众对视频内容的情绪反应.
  • 通过创建一个大规模的注释数据集来解决视频情感理解中的数据限制.
  • 为分析视频广告中的情绪提供准确且易于使用的工具.

主要方法:

  • 利用了超过3万个视频广告的数据集,每广告约有75个观众的约230万个情感注释.
  • 采用卷积神经网络集成视频和音频数据来预测5秒钟的情绪.
  • 收集了八种情绪类别的注释:愤怒,蔑视,厌恶,恐惧,幸福,悲伤,惊喜和中立,并注意到它们的时间发作.

主要成果:

  • 该模型在预测突出的5秒情感片段方面实现了43.6%的平均平衡精度.
  • 在检测特定情绪方面表现出很高的表现,对快乐的准确率为55.8%,对悲伤的准确率为60.2%.
  • 当应用到全广告来确定情感下调时,达到75%的强平均曲线下面面积 (AUC).

结论:

  • 这项研究成功地克服了以前在视频情感理解方面的数据限制.
  • 开发的深度学习模型为预测视频中观众情绪提供了准确的解决方案.
  • 经过培训的网络可用于研究目的,促进该领域的进一步进步.