动作注意力引导的关系推理对弱监督的集团活动的认可
概括
这项研究引入了一种新的运动引导面具生成器 (MGMG) 和运动注意力引导关系推理 (MAGRR) 框架,通过生成多样化的代币嵌入和专注于演员运动来改善弱监督的群体活动识别.
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:
- 现有的以注意力为基础的方法,用于对弱监督的群体活动的识别,难以产生多样化的代币嵌入.
- 无标签的方法需要强大的机制来识别和专注于现场内的相关行为者.
研究的目的:
- 开发一个新的框架,用于认可弱监督的集团活动,以增强代币嵌入多样性.
- 提高对行为体运动区域的关注度,以便更准确地识别活动.
主要方法:
- 一个运动引导面具生成器 (MGMG) 模块被开发出来,用于估计注意区域面具,使用从运动方向衍生的灰度运动面具.
- MGMG 包含一个关联层,同位数注意力,面具生成器和一个专门的激活功能.
- 为MGMG模块定制了一个正常化的相对错误损失函数,以处理值范围不匹配.
- 提出了一种运动注意引导关系推理 (MAGRR) 框架,利用MGMG和时空聚合堆 (SAS) 来激活注意区域和捕获时间依赖.
主要成果:
- 拟议的MAGRR框架在集体活动和集体活动扩展数据集上取得了最先进的表现.
- 在排球和NBA数据集上展示了竞争性表现.
- MGMG模块有效地产生了多样化的代币嵌入,并将注意力集中在演员运动上.
结论:
- 开发的MGMG和MAGRR框架显著推进了对弱监管集团活动的认可.
- 该方法为基于注意力的方法提供了强大的解决方案,以代币嵌入多样性和演员重点面临挑战.
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