一种基于概率比率的方法来对未知对象进行细分
Nazir Nayal1,2, Youssef Shoeb3,4, Fatma Güney1,2
1Computer Engineering Department, Koç University, Istanbul, Turkey.
概括
本研究引入了一个轻量级模块,用于在大型基础模型中进行强大的分销之外 (OoD) 细分. 这种新的方法增强了未知物体检测,而不破坏模型的核心表示,设置了一个新的最先进的状态.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 分布之外 (OoD) 的细分对于开放世界的AI感知系统至关重要.
- 大型基础模型提供了强大的表示,但它们的OOD功能未被充分探索.
- 目前的异常值监督方法破坏了已学习的特征,对于大型模型来说是不可行的.
研究的目的:
- 在大型基础模型中开发一个有效的异常监督方法,用于OoD细分.
- 为了提高Out-of-Distribution检测,而不影响模型的现有特征表示.
- 在检测未知物体方面实现最先进的性能.
主要方法:
- 提出了一种适应性,轻量级的未知估计模块 (UEM),用于异常值监督.
- UEM学习异常值和已知的类的分布.
- 引入了基于概率比率的评分功能,将UEM信心与先前的网络预测融合在一起.
- 制定了一个目标,直接优化异常值得分.
主要成果:
- 在多个分销之外的细分基准上取得了新的最先进的表现.
- 在平均精度方面,其性能比以前的方法高出5.74%.
- 与现有方法相比,证明了较低的错误阳性率.
- 保持强的内线细分业绩.
结论:
- 拟议的未知估计模块 (UEM) 有效地增强了分布之外的细分.
- 这种方法为大型基础模型的异常监管提供了一种非破坏性的方法.
- 该方法为强大的开放世界感知系统设定了新的标准.
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