Understanding Convolution for Semantic Segmentation读书笔记 本文主要是对上采样和dilated convolution进行了修改,优点在于:1.扩大网络的感受野,以聚集更多的全局信息,2.解决由标准dilated convolution所引起的"gridding效应",最后在cityscapes达到很好的效果。 DUC:不同于
Understanding Convolution for Semantic Segmentation from UCSD & CMU & UIUC & TuSimple paper link 这篇文章提出了对深度语义分割网络的两点改进,包括: 1. 一种高效上采样方法以恢复分割结果的分辨率(Dense Upsampling Convolution, DUC); 2. 一种... ...
感觉Understanding Convolution for Semantic Segmentation 相对于 FCN 就如同 YOLO 相对于 RCNN 一样,前者直接省去了层层叠加的上采样、直接一口气上采样,后者省去了 Proposal 过程,直接在图像上开始回归和分类。 真没想到一口气上采样也能学习得更好, @Zijun Deng 提出的第二问题我想了下,由于 DUC 之前的 featur...
Understanding Convolution for Semantic Segmentationhttps://arxiv.org/abs/1702.08502v1模型https://goo.gl/DQMeun 针对语义分割问题,我们从两个方面进行改善,一个是dense upsampling convolution (DUC) 代替 Bilinear upsampling,另一个是用 hybrid dilated convolution (HDC) 代替 传统的 dilated convolution。 3.1. ...
另一种做法是反卷积deconvolution,参考:2015-ICCV-Learning Deconvolution Network for Semantic Segmentation (引用量:3600+)。受到图像超像素算法的启发(2016-CVPR-Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network), 提出了DUC(dense upsampling convolution)...
主要提出DUC(dense upsampling convolution)和HDC(hybrid dilated convolution),其中DUC相当于用通道数来弥补卷积/池化等操作导致的尺寸的损失,HDC为了消除在连续使用dilation convolution时容易出现的gridding effect。 1. DUC * 标准的bilinear interpolation是没有参数需要学习的,对于像素级的分割任务,会造成部分细节信息丢...
Cottrell, "Understanding convolution for semantic segmentation," in IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018, pp. 1451-1460.P. Wang, P. Chen, Y. Yuan, D. Liu, Z. Huang, X. Hou, and G. Cottrell. Understanding convolution for semantic seg- mentation. ...
参考链接:语义分割--Understand Convolution for Semantic Segmentation 知乎链接:https://zhuanlan.zhihu.com/p/26659914 论文链接:https://arxiv.org/abs/1702.08502 github链接:(官方)https://github.com/TuSimple/TuSimple-DUC (非官方)https://github.com/ycszen/pytorch-segmentation 自己的阅读论文笔记:https:/...
If you find the repository is useful for your research, please consider citing: @article{wang2017understanding,title={Understandingconvolution for semantic segmentation},author={Wang,PanquandChen, PengfeiandYuan, YeandLiu, DingandHuang, ZehuaandHou, XiaodiandCottrell, Garrison},journal={arXivpreprint ...
For those who might want to use the convolution function above on a different image or to test out different filters for edge detecting or other image processing tasks, this section is a quick guide on how to do so. The function takes 3 parameters, namely, ‘image_filepath’, ‘filter’...