骨架运动拓蒙面预测和对比学习用于自我监督的人类动作识别
Yan Hui1, Fengyu Li2, Xiuhua Hu2
1College of Science and Engineering, Xi'an Technological University, Xi'an, 710021, China. yanxh_xatu@163.com.
Scientific reports
|February 10, 2026
概括
这项研究引入了一种用于自我监督的人类行动识别的混合框架,通过联合建模骨拓和运动动态来提高准确性. 该方法可以在具有挑战性的条件下提高性能,例如闭塞和有限的数据.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 自主监督的人类行动识别面临着数据增强和联合依赖模型的挑战.
- 现有的方法可能会过度强调突出运动区域或局部关节轨迹.
研究的目的:
- 提出一个新的混合框架,整合拓蒙面运动建模和对比学习,以提高自我监督的人类行动识别.
- 为了解决数据增强和联合依赖性忽视的局限性.
主要方法:
- 开发了一种编码骨拓和运动动态的运动拓掩盖技术.
- 采用多阶段混合增强策略,用于对比学习中的多种积极对.
- 引入了轨迹引导的特征抛落模块,以防止过度关注本地联合轨迹.
主要成果:
- 在NTU-60,NTU-120和PKU-MMD数据集上实现了卓越的性能.
- 在封闭的场景和低监督条件中表现出显著的改善.
- 有效地减轻视觉干扰和注释稀缺性.
结论:
- 拟议的框架通过自我监督学习有效地利用大规模的未标记的骨架数据.
- 显著减少对人类行动识别的昂贵注释数据集的依赖.
- 在复杂的环境和数据限制中提供一个强大的解决方案,用于精确的动作识别.
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