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.
Frontiers in neuroscience
|July 21, 2023
概括
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.
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.
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