建模自我监督的基于骨架的动作识别的内部和上下文注意力
Wentian Xin1, Yue Teng2, Jikang Zhang2
1School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
本研究介绍了MICA,一种新的语言骨架对比学习框架,用于基于骨架的自主监督动作识别. 通过解决模式不平衡和利用频率模式来提高精度,MICA提高了特征表示.
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:
- 多模式对比学习在自我监督的基于骨架的动作识别中表现出色.
- 现有的方法存在模式不平衡,阻碍了对齐和特征表示.
- 这导致模式之间的混,并削弱了行动识别方面的表现.
研究的目的:
- 为了克服基于骨架的动作识别中的模式失衡.
- 探索模式内自相似的特征和模式间实例的交叉一致性.
- 开发一种新的语言骨架对比学习框架,以提高动作识别.
主要方法:
- 提出了一个新的语言骨架对比学习框架 (MICA).
- 引入特征调制,以创建一个骨架语言的动作概念域.
- 开发了使用频域空间时间块 (FreST) 开发的频率特征学习,专注于频域中的关键关节.
主要成果:
- 在NTU RGB+D 60和NTU RGB+D 120数据集上实现了显著的动作识别性能.
- 在PKU-MMD数据集上显示了显著的改进,表现至少比经典方法高4.6%.
- 有效地捕获内部和上下文注意力信息,以进行强大的动作识别.
结论:
- 拟议的MICA框架有效地解决了基于骨架的行动认可中的模式不平衡.
- 通过频域分析利用全球视角和焦点适应,增强特征表示.
- MICA为推进自我监督的基于骨架的动作识别提供了一个有希望的方法.
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