骨-CutMix:将骨与概率性骨交换混合起来,用于监督的域调整
概括
骨架-CutMix通过制造混合域数据来增强基于骨架的动作识别. 这种增强框架通过智能地在源域和目标域之间交换骨架骨来提高模型的稳定性和准确性.
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:
- 基于骨架的动作识别中的域调整通常依赖于域对齐的复杂损失函数.
- 现有的方法很难有效地利用骨架数据固有的结构性质.
- 减轻域名转移对于在不同数据集和条件下强大的性能至关重要.
研究的目的:
- 引入Skeleton-CutMix,这是一个新的增强框架,用于基于骨架的动作识别中监督域调整.
- 通过专注于骨架数据的内在特征来解决现有的域调整技术的局限性.
- 提高行动识别模型的概括性和稳定性.
主要方法:
- 开发了Skeleton-CutMix,这是一个框架,通过在源域和目标域之间交换骨来制造混合域骨架数据.
- 实施了特定类别的骨采样策略,以优先考虑对特定行动类别的重要骨的交换.
- 使用这些制造骨增强训练数据来训练更一般和更强大的模型.
主要成果:
- 证明了骨交换增强策略的效率和有效性.
- 展示了独特的运动特征的保存,同时成功地混合了跨领域的动作和风格.
- 在跨数据集和跨年龄设置 (NTU-60,ETRI-Activity3D) 上,行动识别准确度的平均增长超过3%.
结论:
- 骨架-CutMix为基于骨架的动作识别提供了一个简单而有效的域调整方法.
- 拟议的增强方法优于以前的域适应技术和其他骨架增强策略.
- 该框架通过利用骨架数据的结构对应性,成功增强了模型的概括性.
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