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JSSE:为零射击事件识别提供联合顺序语义编码器.

Naveen Madapana1, Juan P Wachs1

  • 1School of Industrial Engineering, Purdue University, West Lafayette, IN, 47906.

IEEE transactions on artificial intelligence
|December 13, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了使用语义属性的零射击事件识别 (ZSER) 的新方法. 拟议的联合顺序语义编码器 (JSSE) 模型有效地识别了像手势和动作这样的看不见的动态事件.

关键词:
行动和手势识别 行动和手势识别活动活动活动.语义描述符 是一个语义描述符.转移学习学习 转移学习零射击学习的学习

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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:

  • 零射击学习 (ZSL) 在识别看不见的对象类别方面表现出色,但在动态事件 (ZSER) 中未得到充分探索.
  • 现有的ZSL研究主要侧重于静态对象识别,在理解动态事件识别方面存在差距.
  • 从可见到不可见的事件类别转移知识需要超越静态对象识别的新方法.

研究的目的:

  • 通过利用语义属性来解决零射击事件识别 (ZSER) 的挑战.
  • 为ZSER研究引入第一个基于属性的手势数据集 (ZSGL).
  • 为ZSER提出和评估一个端到端的模型,即联合序列语义编码器 (JSSE).

主要方法:

  • 使用亚马逊机械土耳其人开发了ZSGL数据集,包含26个手势类别和65个属性.
  • 使用可训练的循环网络和3D卷积神经网络 (CNN) 来提取时空特征.
  • 为端到端的ZSER提出了联合顺序语义编码器 (JSSE) 模型,优化语义和分类任务.

主要成果:

  • 与现有的基线相比,JSSE模型在四个实验条件 (类别内,跨类别,封闭集,开放集) 中表现优越.
  • 对ZSGL,UCF和HMDB数据集的评估证实了JSSE在零射击事件识别方面的有效性.
  • JSSE取得了有利的结果,在识别可见和不可见的动态事件方面表现优于其他方法.

结论:

  • 拟议的JSSE模型通过使用语义属性为零射击事件识别提供了有效的解决方案.
  • ZSGL数据集为推进基于属性的ZSER的研究提供了宝贵的资源.
  • 这项工作突出了语义属性转移在零射击环境中识别动态事件的潜力.