更多的信号对检测有意义:整合语言知识和频率表示,以提高细粒度飞机识别
Xueru Xu1, Zhong Chen1, Yuxin Hu1
1School of Artificial Intelligence and Automation, National Key Laboratory of Multispectral Information Intelligent Processing Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
概括
这项研究介绍了一种新的细粒度飞机探测器,该探测器集成了语言知识和频率表示. 拟议的方法通过有效地挖掘和保留关键视觉信号,显著提高了检测性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 对象检测正在迅速发展,细粒度检测成为一个关键领域.
- 现有的细粒度探测器面临着不足和丢失关键信号的挑战,阻碍了性能.
- 利用语言和频率域信息可以增强细粒度识别.
研究的目的:
- 提出一种新的细粒度飞机探测器,将语言知识和频率表示整合起来.
- 解决现有探测器在感知和保留关键信号方面的局限性.
- 提高一阶段检测范式的性能,以实现细粒度识别.
主要方法:
- 开发了一个适应频率增强分支 (AFAB) 用于富里埃域处理.
- 实施了内容意识的全球功能强化器 (CGFI) 来增强功能空间.
- 引入了一种微细的文本图像交互式料器 (FTIF) 用于多式联运信息补充.
主要成果:
- 拟议的探测器在光学和SAR图像上表现出卓越的性能.
- FTIF组件显著提高了细粒度识别性能.
- AFAB和CGFI有效地挖掘并保留了关键的视觉内容.
结论:
- 整合语言知识和频率表示为细粒度检测提供了一个有希望的方向.
- 拟议的组件有效地解决了信号不足和细粒度识别损失.
- FTIF模块是一个多功能插件,用于增强现有的单阶段探测器.
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