阅读小结：A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection

时间 2020-12-20

标签深度学习 CNN 物体检测繁體版

原文原文链接

arXiv: https://arxiv.org/pdf/1704.03414.pdf What: 1. 目标是去增强检测器对于遮挡和形变的泛化能力 2. 但是数据集中遮挡和形变的图像一般较少 3. 所以作者提出了 adversary的方法去增加训练难度 4. 整个网络是基于 Fast-RCNN 而不是 Faster-RCNN How: 1. 先pretrain Fast-RC

>>阅读原文<<

1. A-Fast-RCNN:Hard Positive Generation via Adversary for Object Detection
2. CVPR17论文有感：A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
3. A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
4. [论文笔记]A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
5. [深度学习论文笔记][CVPR 17 oral]A-Fast-RCNN： Hard Positive Generation via Adversary for Object Detection
6. 对抗学习用于目标检测--A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
7. 论文阅读-《Focal Loss for Dense Object Detection》
8. CVPR 2018 SINT++:《SINT++: Robust Visual Tracking via Adversarial Hard Positive Generation》论文笔记
9. Focal Loss for Dense Object Detection论文阅读
10. Feature Pyramid Networks for Object Detection 阅读笔记
更多相关文章...
• RSS 阅读器 - RSS 教程
• PHP 实例 - AJAX RSS 阅读器 - PHP教程
• JDK13 GA发布：5大特性解读
• 算法总结-回溯法

最新文章

1. Appium入门
2. Spring WebFlux 源码分析(2)-Netty 服务器启动服务流程 --TBD
3. wxpython入门第六步（高级组件）
4. CentOS7.5安装SVN和可视化管理工具iF.SVNAdmin
5. jedis 3.0.1中JedisPoolConfig对象缺少setMaxIdle、setMaxWaitMillis等方法，问题记录
6. 一步一图一代码，一定要让你真正彻底明白红黑树
7. 2018-04-12—（重点）源码角度分析Handler运行原理
8. Spring AOP源码详细解析
9. Spring Cloud（1）
10. python简单爬去油价信息发送到公众号

本站公众号

欢迎关注本站公众号,获取更多信息

1. A-Fast-RCNN:Hard Positive Generation via Adversary for Object Detection
2. CVPR17论文有感：A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
3. A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
4. [论文笔记]A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
5. [深度学习论文笔记][CVPR 17 oral]A-Fast-RCNN： Hard Positive Generation via Adversary for Object Detection
6. 对抗学习用于目标检测--A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
7. 论文阅读-《Focal Loss for Dense Object Detection》
8. CVPR 2018 SINT++:《SINT++: Robust Visual Tracking via Adversarial Hard Positive Generation》论文笔记
9. Focal Loss for Dense Object Detection论文阅读
10. Feature Pyramid Networks for Object Detection 阅读笔记

>>更多相关文章<<