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相关概念视频

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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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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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对比的自我监督的表现学习,没有负样本,用于多式模式的人类行动识别.

Huaigang Yang1, Ziliang Ren1,2, Huaqiang Yuan1

  • 1School of Computer Science and Technology, Dongguan University of Technology, Dongguan, China.

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概括
此摘要是机器生成的。

这项研究引入了使用多式联络数据 (如骨架序列和IMU信号) 进行动作识别的新框架. 该方法提高了不需要负样本的性能,在各种学习场景中提高了准确性.

关键词:
变压器 变压器 变压器相反的自我监督学习学习.功能编码器的特征编码器人类行动承认承认多式联络方式的代表性.

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 人与计算机的交互

背景情况:

  • 动作识别对于人机交互至关重要.
  • 目前的方法,特别是卷积神经网络 (ConvNets),由于缺乏足够的大规模标记数据而受到限制.
  • 多模式特征表示提供了由于补充数据源的潜在改进.

研究的目的:

  • 提出一种新的多式联运特征表示和对比的自我监督学习框架.
  • 为了提高动作识别性能和模型概括能力.
  • 解决现有方法中数据稀缺性的局限性.

主要方法:

  • 一个新的框架,利用两个分支之间的重量共享,用于多式联运特征学习.
  • 不需要负样本的对比自主监督学习方法.
  • 骨架序列和惯性测量单元 (IMU) 信号的集成,以实现强大的特征提取.

主要成果:

  • 拟议的框架有效地从未标记的多式联运数据中学习有用的表示.
  • 与单模和多模基线相比,表现出优异的性能.
  • 在行动检索,半监督和零射击学习的UTD-MHAD和MMAct基准上取得了最先进的结果.

结论:

  • 开发的框架提供了一个有效的解决方案,以有限的标记数据来识别行动.
  • 多模式自主监督学习是改善模型概括的一个有希望的方向.
  • 该方法成功地利用了骨架和IMU数据的互补信息.