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双向语义一致性指导的对比嵌入用于生成的零射击学习.

Zhengzhang Hou1, Zhanshan Li2, Jingyao Li3

  • 1College of Software, Jilin University, Changchun, 130012, Jilin, China; Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.

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概括
此摘要是机器生成的。

本研究介绍了一种双向语义一致性指导 (BSCG) 模型,通过确保合成特征的语义一致性来改善生成零射击学习. BSCG模型增强了对未见类的知识转移和概括.

关键词:
协作式学习是一种协作式学习.相反的学习学习.语义指导的语义指导变压器变压器变压器零射击学习的学习.

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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:

  • 生成式零射击学习 (GZSL) 方法使用图像和语义信息合成未见类的特征.
  • 现有的GZSL方法往往无法确保合成特征的语义一致性,因为依赖于全局图像特征,导致差异化能力较差.
  • 在GZSL中,从可见到不可见类的知识转移方面的偏见仍然是一个挑战.

研究的目的:

  • 为改进GZSL提出一种新的双向语义一致性引导 (BSCG) 生产模型.
  • 为未见类增强合成特征的语义一致性和区分能力.
  • 确保强大的知识转移,并提高GZSL模型的泛化能力.

主要方法:

  • 拟议的BSCG模型采用双向语义指导框架 (BSGF).
  • BSGF集成了属性对视觉指导 (AVG) 和视觉对属性指导 (VAG),以增强视觉语义交互.
  • 引入了对比一致性空间 (CCS),通过增加类内紧性和类间分离性来优化特征质量.

主要成果:

  • 与最先进的方法相比,BSCG模型显示出显著的性能改进.
  • 实验对传统和通用零射击学习设置的三个基准数据集进行了实验.
  • 提出的方法有效地解决了语义一致性的局限性,并具有可区分性.

结论:

  • 通过确保语义一致性,BSCG模型为生成零射击学习提供了强大的解决方案.
  • BSGF和CCS的整合带来了卓越的知识转移和概括能力.
  • 该方法显示了推动零射击学习研究的巨大潜力.