自主监督的3D动作表示学习与骨架云色化
IEEE transactions on pattern analysis and machine intelligence
|October 19, 2023
概括
这项研究引入了一种新的骨架云色化方法,用于自主监督的3D动作识别. 该技术从未标记的数据中学习时空特征,在各种学习环境中显著提高了性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 监督学习主导着基于3D骨架的动作识别,但需要昂贵的标记数据.
- 没有标记的骨架数据对有效的行动表示学习提出了挑战.
研究的目的:
- 开发一种自主监督的方法,从未标记的3D骨架数据中学习时空表示.
- 通过减少对广泛的手动注释的依赖来推进3D动作识别.
主要方法:
- 提出了一种新的骨架云彩色化技术,将序列表示为3D点云,并根据时空秩序赋予基于人工颜色标签.
- 一个具有双流预训练网络 (细粒度和粗粒度色化) 的自动编码器框架旨在学习多级特征.
- 面具骨云重新绘制任务是为了预训练自动编码器来学习信息表示而引入的.
主要成果:
- 骨架云彩色化方法在无监督和半监督的3D动作识别中表现出卓越的性能.
- 该方法在完全监督和转移学习环境中实现了竞争力的结果,跨越多个基准数据集 (NTU RGB+D,NTU RGB+D 120,PKU-MMD,NW-UCLA,UWA3D).
结论:
- 通过骨架云颜色化进行自主监督学习对于3D动作识别是有效的.
- 拟议的方法为监督方法提供了可行的替代方案,减少了注释负担,提高了未标记数据的性能.
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