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基于混合神经网络的低层次视觉递归滤波器学习Learning Recursive Filters for Low-Level Vision via a Hybrid Neural Network |
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| 课程网址: | http://videolectures.net/eccv2016_liu_recursive_filters/ |
| 主讲教师: | Sifei Liu |
| 开课单位: | 加州大学默塞德分校电气工程与计算机科学系 |
| 开课时间: | 2016-10-24 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this paper, we consider numerous low-level vision problems (e.g., edge-preserving filtering and denoising) as recursive image filtering via a hybrid neural network. The network contains several spatially variant recurrent neural networks (RNN) as equivalents of a group of distinct recursive filters for each pixel, and a deep convolutional neural network (CNN) that learns the weights of RNNs. The deep CNN can learn regulations of recurrent propagation for various tasks and effectively guides recurrent propagation over an entire image. The proposed model does not need a large number of convolutional channels nor big kernels to learn features for low-level vision filters. It is significantly smaller and faster in comparison with a deep CNN based image filter. Experimental results show that many low-level vision tasks can be effectively learned and carried out in real-time by the proposed algorithm. |
| 关 键 词: | 视觉递归; 神经网络; 循环传播 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-04-22：chenxin01 |
| 最后编审: | 2023-05-18：chenxin01 |
| 阅读次数: | 24 |