联合语义编码器 (JSE):用于零射击手势学习的联合语义编码器
1School of Industrial Engineering, Purdue University, West Lafayette IN 47906, United States.
概括
本研究介绍了联合语义编码器 (JSE),用于手势识别 (ZSGL) 中的零射击学习. 通过有效利用特征提取技术和语义信息,JSE显著提高了性能.
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) 允许使用描述识别未见的类别.
- 手势识别 (ZSGL) 是ZSL的一个未经探索的领域,在特征选择方面缺乏研究.
- 深度学习往往减少了对功能工程的需求,但对于稀缺数据来说,域名知识至关重要.
研究的目的:
- 调查速度,启发式和潜伏特征对ZSGL性能的影响.
- 为 ZSGL 提出一种新的双线自动编码方法,即联合语义编码器 (JSE).
- 评估与现有的ZSL方法相比,JSE的有效性.
主要方法:
- 开发了联合语义编码器 (JSE),一个双线自动编码器.
- 同时减少重建,语义和分类损失.
- 将JSE与现有的ZSL方法进行比较,使用基于属性和跨类别的场景.
主要成果:
- 在基于属性的分类中,无论特征类型如何,JSE的表现优于其他方法5% (p<0.01).
- 在跨类别设置中接受启发性特征训练时,JSE显示出显著的绩效增长 (p<0.01).
结论:
- 拟议的JSE模型在零射击手势识别方面表现出卓越的性能.
- 特征提取技术显著影响ZSGL性能,启发式特征显示有希望.
- 证券交易所为ZSGL提供了一个强大的解决方案,特别是在数据稀缺的情况下.
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