基于注意力的变化自编码模型,用于通过世代识别人与人之间的交互
Bonny Banerjee1, Murchana Baruah1
1Institute for Intelligent Systems, and Department of Electrical & Computer Engineering, University of Memphis, Memphis, TN 38152, USA.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
这项研究引入了新的多式联络代理模型,用于从3D骨数据中预测人类意图. 这些模型提供与最先进的方法具有较少参数的可比精度,推进人工智能应用.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
背景情况:
- 人类意图预测对于开发至关重要,并具有众多应用.
- 现有的意图预测模型是有限的.
- 意图预测的早期发展凸显了它的重要性.
研究的目的:
- 提出基于注意力的新型代理模型,用于预测3D骨相互作用的意图.
- 开发具有内在多式模式的模型,具有感知和自身感知途径.
- 提高意图预测模型的效率和准确性.
主要方法:
- 利用两种基于注意力的代理模型,通过见取样3D骨.
- 在代理设计中纳入多式途径 (感知和自身感知).
- 训练有素的代理人通过学习最佳采样策略来最大限度地减少生成和分类错误.
主要成果:
- 一个拟议的模型实现了与最先进的技术状态相比的分类和生成准确性.
- 有效的模型包含的可训练参数比现有方法少.
- 对基准数据集进行了评估,并与最先进的模型进行了对比.
结论:
- 拟议的多式联络代理模型显示了准确和高效的意图预测的前景.
- 这些模型的洞察力可以指导未来人工智能代理商的开发.
- 这些发现有助于人类机器人交互和自主系统的进步.
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