文本衍生关系图增强网络,用于基于骨架的行动细分
概括
本研究介绍了一种新的文本衍生关系图增强网络 (TRG-Net),用于基于骨架的时间行动细分. TRG-Net通过利用大型语言模型进行增强的建模和监督来提高人类运动理解,实现最先进的结果.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 基于骨的时间动作细分 (STAS) 方法分析人类骨运动以进行动作识别.
- 现有的STAS模型往往无法捕捉关节和动作的内在相关性.
- 目前的框架智能分类依赖于一热编码和交叉损失,限制了运动理解.
研究的目的:
- 通过整合来自大型语言模型 (LLM) 的先前知识来增强STAS.
- 开发一个新的网络,TRG-Net,用于改进时空建模和行动细分监督.
- 解决捕捉联合行动相关性的局限性,并改善整体的人类运动识别.
主要方法:
- 拟议的文本衍生关系图增强网络 (TRG-Net) 使用LLM生成的图形.
- 动态时空融合建模 (DSFM) 与文本衍生联合图 (TJG) 用于空间关系建模.
- 绝对相对类间监督 (ARIS) 使用对比学习和文本衍生动作图 (TAG).
- 空间意识增强处理 (SAEP) 随机关节封闭和轴旋转进行概括.
主要成果:
- 在四个公共数据集中,TRG-Net表现出卓越的性能.
- 提出的方法 (DSFM,ARIS,SAEP) 显著提高了行动细分的准确性.
- 来自LLM的图表有效地增强了STAS的建模和监督方面.
结论:
- TRG-Net代表了基于骨架的时间动作细分的重大进步.
- 利用LLM来生成关系图表为改善人类运动分析提供了一个有希望的方向.
- 开发的技术从骨数据中提供了对人类行为的更全面的理解.
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