推理以行动为中心的时间关系在丰富的特征层次上为行动识别
Manshu Liang1, Wenbin Wu1, Zhuolei Chen1
1Electric Power Science Research Institute, State Grid Fujian Electric Power Co. Ltd., Fujian, China.
PloS one
|July 24, 2025
概括
本研究引入了一个以行动为中心的时间关系推理 (ATR) 块,以改善行动识别. 通过专注于与行动相关的区域,ATR增强了时间推理,从而带来了显著的绩效增长.
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:
- 行动识别对于理解人类活动至关重要.
- 现有的方法通常依赖于高级语义和背景信息,限制了性能.
- 时间关系推理是关键的,但很难有效地实施.
研究的目的:
- 提出一个新的以行动为中心的时间关系推理 (ATR) 块,以改进动作识别.
- 发展一个以行动为中心的和层次化的方法,以时间关系推理.
- 提高行动识别模型的效率和有效性.
主要方法:
- 引入了一个新的以行动为中心的时间关系推理 (ATR) 块.
- ATR由一个与行动相关的区域定位器 (ARL) 和一个以行动为中心的有效推理器 (EAR) 组成.
- 在不同的深度,ATR块被集成到现有的动作识别模型 (TSN,TSM,SlowOnly) 中.
主要成果:
- 在所有基准指标 (Something-Something V1,V2,Kinetics) 中,在动作识别准确度上观察到一致和显著的改进.
- 拟议的ATR块在以行动为中心和层次的时间关系推理方面表现出有效性.
- 该方法在微小的计算开销下实现了这些收益.
结论:
- 通过专注于以行动为中心的时间关系,ATR块显著提高了动作识别.
- 层次和以行动为中心的表述对于时间关系推理是有效的.
- 这种方法对现实世界的动作识别应用具有实际意义.
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