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:不同于传统的一次性回复全分辨率的label Map,我们通过学习...
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... ...
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. ...
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...
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...
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)...
Cottrell. Understanding convolution for semantic seg- mentation. In IEEE Winter Conf. on Applications of Com- puter Vision (WACV), 2018. 7Wang, P., et al.: Understanding convolution for semantic segmentation. In: WACV (2018)P. Wang, P. Chen, Y. Yuan, D. Liu, Z. Huang, X. Hou, ...
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 ...
It was originally developed for the ImageNet VID challenge introduced in ILSVRC2015. It contains components such as region proposal, still-image object detection, generic object tracking, spatial max-pooling and temporal convolution. Reference: Object Detection from Video Tubelets with Convolutional ...