READING NOTE: R-FCN: Object Detection via Region-based Fully Convolutional Networks
来源:互联网 发布:逆波兰式算法的栈图 编辑:程序博客网 时间:2024/05/16 13:58
TITLE: R-FCN: Object Detection via Region-based Fully Convolutional Networks
AUTHER: Jifeng Dai, Yi Li, Kaiming He, Jian Sun
ASSOCIATION: MSRA, Tsinghua University
FROM: arXiv:1605.06409
CONTRIBUTIONS
- A framework called Region-based Fully Convolutional Network (R-FCN) is develpped for object detection, which consists of shared, fully convolutional architectures.
- A set of position-sensitive score maps are introduced to enalbe FCN representing translation variance.
- A unique ROI pooling method is proposed to shepherd information from metioned score maps.
METHOD
- The image is processed by a FCN manner network.
- At the end of FCN, a RPN (Region Proposal Network) is used to generate ROIs.
- On the other hand, a score map of
k2(C+1) channels is generated using a bank of specialized convolutional layers. - For each ROI, a selective ROI pooling is utilized to generate a
C+1 channel score map. - The scores in the score map are averaged to vote for category.
- Another
4k2 dim convolutional layer is learned for bounding box regression.
Training Details
- R-FCN is trained end-to-end with pre-computed region proposals. Both category and position are learnt with the loss function:
L(s,tx,y,w,h)=Lcls(sc∗)+λ[c∗>0]Lreg(t,t∗) . - For each image, N proposals are generated and B out of N proposals are selected to train weights according to the highest losses. B is set to 128 in this work.
- 4-step alternating training is utilized to realizing feature sharing between R-FCN and RPN.
ADVANTAGES
- It is fast (170ms/image, 2.5-20x faster than Faster R-CNN).
- End-to-end training is easier to process.
- All learnable layers are convolutional and shared on the entire image, yet encode spatial information required for object detection.
DISADVANTAGES
- Compared with Single Shot methods, more computation resource is needed.
0 0
- READING NOTE: R-FCN: Object Detection via Region-based Fully Convolutional Networks
- READING NOTE: R-FCN: Object Detection via Region-based Fully Convolutional Networks
- R-FCN: Object Detection via Region-based Fully Convolutional Networks
- R-FCN: Object Detection via Region-based Fully Convolutional Networks
- R-FCN: Object Detection via Region-based Fully Convolutional Networks
- R-FCN: Object Detection via Region-based Fully Convolutional Networks
- R-FCN: Object Detection via Region-based Fully Convolutional Networks
- R-FCN: Object Detection via Region-based Fully Convolutional Networks
- R-FCN: Object Detection via Region-based Fully Convolutional Networks
- [论文阅读]R-FCN: Object Detection via Region-based Fully Convolutional Networks
- 论文笔记 | R-FCN: Object Detection via Region-based Fully Convolutional Networks
- R-FCN: Object Detection via Region-based Fully Convolutional Networks (NIPS 16), Arxiv 16.05
- RCNN学习笔记(11):R-FCN: Object Detection via Region-based Fully Convolutional Networks
- [论文阅读]R-FCN: Object Detection via Region-based Fully Convolutional Networks
- 论文笔记 R-FCN: Object Detection via Region-based Fully Convolutional Networks
- R-FCN:Object Detection via Region-based Fully Convolutional Networks论文部分总结学习
- 对论文R-FCN: Object Detection via Region-based Fully Convolutional Networks的阅读
- 论文阅读 R-FCN: Object Detection via Region-based Fully Convolutional Networks
- 多项式混合模型
- 如何面对ubuntu mysql ----->>> Access denied for user 'root'@'localhost'
- Axure 8.0中文版下载(支持windows和Mac)
- Java Mail 发送带附件邮件
- java中文乱码问题----常见编码类型
- READING NOTE: R-FCN: Object Detection via Region-based Fully Convolutional Networks
- C++ limits头文件
- 微信运营必须收藏的软件工具网站合集
- 数据库的操作
- 数字1的数量
- HashMap实现原理小结
- 【程序人生】:学习方法
- 设计模式 - Flyweight模式
- Android开发之使用MediaRecorder录制声音