Hyperspectral Images Classification Based on Dense Convolutional Networks with Spectral-Wise Attention Mechanism

借鉴了DenseNet的思想,用了空洞卷积而不是池化,使得特征图不会缩小,因此每个dense连接都可以直接连,最后一层是包括了前面所有层的特征图. 此外还加入了channel-wise的注意力,对每个通道进行筛选. 做的还是逐像素预测,选取一个像素点周围d的大小输入到网络中,得到标签. 补充: DenseNet 结构: 内存开销巨大.…

论文笔记：ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks

ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks2018-03-05 11:13:05 1. 引言: 本文尝试用基于四个方向的 RNN 来替换掉 CNN中的 convolutional layer(即:卷积+Pooling 的组合).通过在前一层的 feature 上进行四个方向的扫描,完成特征学习的过程. The recurrent layer ensures that each…

HYPERSPECTRAL IMAGE CLASSIFICATION USING TWOCHANNEL DEEP CONVOLUTIONAL NEURAL NETWORK阅读笔记

HYPERSPECTRAL IMAGE CLASSIFICATION USING TWOCHANNEL DEEP CONVOLUTIONAL NEURAL NETWORK 论文地址:https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7730324 1.文章简介: 该论文是用双通道卷积神经网络CNN分别提取空谱信息,然后将得到的抽象特征级联为全连接层的输入,以此作为空谱联合信息输入两层全连接层以及softmax层.此外,文中针对小…

论文笔记之：Semi-supervised Classification with Graph Convolutional Networks

Semi-supervised Classification with Graph Convolutional Networks 2018-01-16 22:33:36 1. 文章主要思想: 2. 代码实现(Pytorch):https://github.com/tkipf/pygcn [Introduction]: 本文尝试用 GCN 进行半监督的分类,通过引入一个 graph Laplacian regularization term 到损失函数中: 其中,L0 代表损失函数,即:gra…

Hyperspectral Image Classification Using Similarity Measurements-Based Deep Recurrent Neural Networks

用RNN来做像素分类,输入是一系列相近的像素,长度人为指定为l,相近是利用像素相似度或是范围相似度得到的,计算个欧氏距离或是SAM. 数据是两个高光谱数据 1.Pavia University,Reflective Optics System Imaging Spectrometer (ROSIS) sensor得到,102个波段,1.3米空间分辨率,总大小610*340像素,9类地物 2.Salinas image,Airborne Visible/Infrared Imaging Spect…

论文翻译——Character-level Convolutional Networks for Text Classification

论文地址 Abstract Open-text semantic parsers are designed to interpret any statement in natural language by inferring a corresponding meaning representation (MR – a formal representation of its sense). 开放文本语义分析器被设计为通过推断相应的意义表示(MR -其意义的正式表示)来解释自然语言中的任何语句.…

Semi-Supervised Classification with Graph Convolutional Networks

Kipf, Thomas N., and Max Welling. "Semi-supervised classification with graph convolutional networks." arXiv preprint arXiv:1609.02907 (2016). 作者对Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering这个工作进行了简化,使之应用于graph节点…

论文解读 - Composition Based Multi Relational Graph Convolutional Networks

1 简介随着图卷积神经网络在近年来的不断发展,其对于图结构数据的建模能力愈发强大.然而现阶段的工作大多针对简单无向图或者异质图的表示学习,对图中边存在方向和类型的特殊图----多关系图(Multi-relational Graph)的建模工作较少,且大多存在着两个问题: (1)整体网络模型的过参数化, (2)仅针对于结点的表示学习. 针对这两个问题,本论文提出了一种基于组合的图卷积神经网络来同时建模结点和边的表示,为了降低大量的边类型带来的参数量,作者采用了向量分解的方式,所有的边类型表示通过…

论文解读（DropEdge）《DropEdge: Towards Deep Graph Convolutional Networks on Node Classification》

论文信息论文标题:DropEdge: Towards Deep Graph Convolutional Networks on Node Classification论文作者:Yu Rong, Wenbing Huang, Tingyang Xu, Junzhou Huang论文来源:2020, ICLR论文地址:download 论文代码:download 1 Introduction 由于 2022 年的论文看不懂,找了一篇 2020 的论文缓解一下心情,我太难了. 提出一种可以缓解过拟合…

《Generative Adversarial Networks for Hyperspectral Image Classification 》论文笔记

论文题目:<Generative Adversarial Networks for Hyperspectral Image Classification> 论文作者:Lin Zhu, Yushi Chen, Member, IEEE, Pedram Ghamisi, Member, IEEE, and Jó n Atli Benediktsson, Fellow, IEEE 论文发表时间:2018 网络简称:3D-GAN 发表期刊:IEEE Transactions on Geoscience…

（转）ResNet, AlexNet, VGG, Inception: Understanding various architectures of Convolutional Networks

ResNet, AlexNet, VGG, Inception: Understanding various architectures of Convolutional Networks by KOUSTUBH this blog from: http://cv-tricks.com/cnn/understand-resnet-alexnet-vgg-inception/ Convolutional neural networks are fantastic for visual…

VGGNet论文翻译-Very Deep Convolutional Networks for Large-Scale Image Recognition

Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan[‡] & Andrew Zisserman[§] Visual Geometry Group, Department of Engineering Science, University of Oxford {karen,az}@robots.ox.ac.uk 用于大规模图像识别的深度卷积网络 Karen Simonyan[‡] &am…

高光谱图像分类简述+《Deep Learning for Hyperspectral Image Classification: An Overview》综述论文笔记

论文题目<Deep Learning for Hyperspectral Image Classification: An Overview> 论文作者:Shutao Li, Weiwei Song, Leyuan Fang,Yushi Chen, Pedram Ghamisi,Jón Atli Benediktsson 论文发表年份:2019 发表期刊:IEEE Transactions on Geoscience and Remote Sensing 一.高光谱简述高光谱成像是一项重要的…

Fully Convolutional Networks for semantic Segmentation（深度学习经典论文翻译）

摘要卷积网络在特征分层领域是非常强大的视觉模型.我们证明了经过端到端.像素到像素训练的卷积网络超过语义分割中最先进的技术.我们的核心观点是建立"全卷积"网络,输入任意尺寸,经过有效的推理和学习产生相应尺寸的输出.我们定义并指定全卷积网络的空间,解释它们在空间范围内dense prediction任务(预测每个像素所属的类别)和获取与先验模型联系的应用.我们改编当前的分类网络(AlexNet [22] ,the VGG net [34] , and GoogLeNet [35] )到完…

Densely Connected Convolutional Networks 论文阅读

毕设终于告一段落,传统方法的视觉做得我整个人都很奔溃,终于结束,可以看些搁置很久的一些论文了,嘤嘤嘤 Densely Connected Convolutional Networks 其实很早就出来了,cvpr 2017 best paper 觉得读论文前,还是把dense net的整个网络结构放到http://ethereon.github.io/netscope/#/editor 上面可视化看一下,会更加容易理解,总体这篇论文很好理解上图是一个5层的dense block,每个dense…

【Semantic Segmentation】 Instance-sensitive Fully Convolutional Networks论文解析(转)

这篇文章比较简单,但还是不想写overview,转自: https://blog.csdn.net/zimenglan_sysu/article/details/52451098 另外,读这篇paper的时候,一直想不明白白一个问题,就是他分出了\(k^2\)个Instance-sensitive score maps,他是怎么训练的..换句话说,ground truth是怎么弄的? 文章里只说了用logistics做损失函数,应该需要看代码,因为还没有搞分割的打算,先不详细了解代码. Inst…

【Semantic segmentation】Fully Convolutional Networks for Semantic Segmentation 论文解析

目录 0. 论文链接 1. 概述 2. Adapting classifiers for dense prediction 3. upsampling 3.1 Shift-and-stitch 3.2 decreasing subsampling 3.3 Deconvolution(backwards strided convolution) 4. Segmentation Architecture 5. Metric @ 0. 论文链接 FCN(https://arxiv.org/abs/14…

Emotion Recognition Using Graph Convolutional Networks

Emotion Recognition Using Graph Convolutional Networks 2019-10-22 09:26:56 This blog is from: https://towardsdatascience.com/emotion-recognition-using-graph-convolutional-networks-9f22f04b244e Recently, deep learning has made much progress in natural…

Fully Convolutional Networks for Semantic Segmentation 译文

Fully Convolutional Networks for Semantic Segmentation 译文 Abstract Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed…

论文阅读（Xiang Bai——【CVPR2016】Multi-Oriented Text Detection with Fully Convolutional Networks）

Xiang Bai--[CVPR2016]Multi-Oriented Text Detection with Fully Convolutional Networks 目录作者和相关链接方法概括方法细节创新点和贡献实验结果问题讨论总结与收获点作者和相关链接作者: paper下载方法概括 Step 1--文本块检测: 先利用text-block FCN得到salient map,再对salient map进行连通分量分析得到text block: Step 2--文本线形成:…

VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION 这篇论文

由Andrew Zisserman 教授主导的 VGG 的 ILSVRC 的大赛中的卷积神经网络取得了很好的成绩,这篇文章详细说明了网络相关事宜. 文章主要干了点什么事呢?它就是在在用卷积神经网络下,在采用小的卷积核与小的移动步长的情况下,探索一下网络的深度对目标识别率的影响. 网络的大体结构网络的输入为224*224的RGB图片,后面跟卷积层,卷积核的大小基本都为3*3有最小的可以保留图片空间分瓣率的卷积核,步长为1个像素,偶尔会有1*1的卷积核,这就相当于加入了一个非线性变换而已.再往后接…

Very Deep Convolutional Networks for Large-Scale Image Recognition

Very Deep Convolutional Networks for Large-Scale Image Recognition 转载请注明:http://blog.csdn.net/stdcoutzyx/article/details/39736509 这篇论文是今年9月份的论文[1],比較新,当中的观点感觉对卷积神经网络的參数调整大有指导作用,特总结之.关于卷积神经网络(Convolutional Neural Network, CNN),笔者后会作文阐述之,读者若心急则或可用谷歌百度一…

[CVPR2017] Weakly Supervised Cascaded Convolutional Networks论文笔记

p.p1 { margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px "Helvetica Neue"; color: #042eee } p.p2 { margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px "Helvetica Neue"; color: #323333 } p.p3 { margin: 0.0px 0.0px 0.0px 0.0px; font: 16.0px "…

【ML】ICLR2016_Delving Deeper into Convolutional Networks

ICLR2016_DELVING DEEPER INTO CONVOLUTIONAL NETWORKS Note here: Ballas recently proposed a novel framework on learning video representation, following is the review note after reading his paper. Link: http://arxiv.org/pdf/1511.06432v4.pdf [Brief intro…