多模式自适应特征融合图形卷积网络,用于基于骨架的动作识别
Haiping Zhang1,2, Xinhao Zhang3, Dongjin Yu1
1School of Computer Science, Hangzhou Dianzi University, Hangzhou 310005, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
这项研究引入了一种新的框架,用于基于骨架的动作识别,使用适应性卷积和特征融合. 多模式自适应特征融合框架 (MMAFF) 增强了接收领域,以改善图形卷积网络中的上下文聚合.
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:
- 图形卷积网络 (GCNs) 在非欧几里德数据方面表现出色,使它们适合基于骨架的动作识别.
- 传统的多尺度时间卷积使用固定的参数,这可能不适合不同的数据集和网络层.
研究的目的:
- 为基于骨架的动作识别开发一个适应性框架,克服固定受体场和上下文聚合的局限性.
- 为了提高图形卷积网络在动作识别任务中的性能.
主要方法:
- 提出了一个多模式自适应特征融合框架 (MMAFF),结合了自适应卷积内核和扩张速率,并具有自我注意机制.
- 引入了功能融合机制来取代剩余连接,改进了上下文聚合和初始功能融合.
- 在MMAFF中利用一个肢体流来处理相关的多模式数据.
主要成果:
- 在MMAFF框架中,可自适应地选择卷积参数,优化跨不同网络层的接收场.
- 特征融合机制有效地解决了上下文聚合和初始特征融合的挑战.
- 该模型在NTU-RGB+D 60和NTU-RGB+D 120数据集上取得了竞争性结果.
结论:
- 拟议的MMAFF框架通过增强空间和时间接收场所,在基于骨架的行动识别方面表现出卓越的表现.
- 适应性机制和改进的特征融合导致更强大的上下文聚合和更好的识别精度.
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