完全木:完全利用多元化质量标签用于弱监督的面向对象检测
概括
本研究介绍了Wholly-WOOD,这是一个用于训练面向物体探测器 (OOD) 的框架,使用弱标签,如点和水平框 (HBoxes). 它实现了接近完全监督方法的性能,降低了注释成本.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 遥感 遥感 遥感 遥感
背景情况:
- 对象检测通常使用水平边界框 (HBoxes),但许多对象需要旋转边界框 (RBoxes) 进行准确的方向估计.
- 训练面向对象探测器 (OOD) 通常需要昂贵的旋转注释.
- 现有的数据集可能具有较弱的注释,如点或HBoxes,这为更有效的培训提供了机会.
研究的目的:
- 开发一个名为Wholly-WOOD的弱监督导向物体探测器 (OOD) 框架.
- 以统一的方式有效利用各种注释类型,包括点,HBoxes和RBoxes.
- 为了减少对劳动密集型轮换注释的依赖,用于训练OOD模型.
主要方法:
- 开发了Wholly-WOOD,这是对低监督OOD的统一框架.
- 实施了一种利用各种标签形式 (积分,HBoxes,RBoxes) 进行培训的方法.
- 通过仅使用HBox训练对RBox训练对手进行评价.
主要成果:
- 在定向物体检测方面,Wholly-WOOD表现出强的性能.
- 仅使用HBoxes训练的结果与使用RBox训练的模型相当.
- 显著减少了面向对象检测任务所需的注释工作.
结论:
- 对OOD进行弱监督的培训是可行的和有效的.
- Wholly-WOOD提供了一种实际的解决方案,用于利用具有较弱注释的现有数据集.
- 该框架在遥感和其他需要定向物体检测的领域具有广泛的应用.
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