干预基于前门标准的几次射击物体检测
Yanan Zhang1, Jiangmeng Li2, Qirui Ji1
1University of Chinese Academy of Sciences, Beijing, China; National Key Laboratory of Space Integrated Information System, Institute of Software, Chinese Academy of Sciences, Beijing, China.
概括
短射击物体检测模型过度适合基本类别. 我们引入了前门调节器,使用因果推理来缓解虚假的相关性,改善新型物体的检测.
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:
- 短拍物体检测 (FSOD) 方法利用基础类别对新类别的知识.
- 现有的FSOD方法假设足够的可转移知识是从基础类别中学到的.
- 我们的研究发现,过度适应基类数据是当前FSOD模型中的一个关键问题.
研究的目的:
- 从因果关系的角度研究FSOD过度装配的影响.
- 提出一种新的方法,前门调节器,以解决虚假的相关性.
- 为了提高少量射击物体检测模型的通用性.
主要方法:
- 开发了一个结构因果模型,其中包括因果和虚假的生成因子.
- 引入了前门调节器,以前门标准为指导.
- 实现了两个插入和运行的规范化术语:语义分组和语义解.
主要成果:
- 证明虚假的生成因素在FSOD中充当混因子.
- 拟议的前门调节器有效地减轻了虚假因素的误导性影响.
- 对基准数据集的实验验验证了拟议方法的改进性能.
结论:
- 对基本类别的过度配置阻碍了少数镜头对象检测的泛化.
- 因果推断提供了一个框架,以理解和解决FSOD中的混因素.
- 前门调节器提供了一个有前途的方向,用于强大的几次射击物体检测.
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