随机频谱转换用于在看不见的领域调整对象探测器.
概括
我们介绍了对随机转换 (MLRT) 的元学习,用于域泛化对象检测 (DGOD). 通过使用随机的频谱转换和梯度平衡来减少域偏差,MLRT增强了跨未见域的模型概括性.
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:
- 域泛化 (DG) 旨在通过对多个源域进行训练,创建在未见数据上表现良好的模型.
- 对象检测在域泛化方面面临挑战,称为域泛化的对象检测 (DGOD).
- 现有的 DG 方法经常存在域偏差,导致对特定源域进行过拟合.
研究的目的:
- 开发一种用于域泛化对象检测 (DGOD) 的新方法.
- 解决现有 DG 对象检测方法中的域偏差问题.
- 提高对象探测器对未见的目标域的概括能力.
主要方法:
- 建议对DGOD进行随机转换 (MLRT) 的元学习.
- 引入一个随机频谱转换 (RST) 模块,通过随机化频率空间信息来增加源域多样性.
- 整合一个梯度加权 (GW) 模块,以平衡跨域的梯度,并减轻域偏差.
主要成果:
- 拟议的MLRT模型,整合RST和GW,在DGOD中证明了有效性.
- 六个基准的实验表明,MLRT实现了最先进的 (SOTA) 性能.
- RST模块成功地转换了域,增加了数据多样性,而GW平衡了域梯度.
结论:
- MLRT有效地学习域不变对象检测器.
- RST和GW的组合显著减少了域偏差,改善了概括.
- 拟议的方法为域泛化对象检测设定了一个新的标准.
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