cvpr2015总结
cvpr所有文章
http://cs.stanford.edu/people/karpathy/cvpr2015papers/
CNN
Hypercolumns for Object Segmentation and Fine-Grained Localization
Bharath Hariharan, Pablo Arbeláez, Ross Girshick, Jitendra Malik
Improving Object Detection With Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
Yuting Zhang, Kihyuk Sohn, Ruben Villegas, Gang Pan, Honglak Lee
Going Deeper With Convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich
Deep Neural Networks Are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Anh Nguyen, Jason Yosinski, Jeff Clune
Deformable Part Models are Convolutional Neural Networks
Ross Girshick, Forrest Iandola, Trevor Darrell, Jitendra Malik
Efficient Object Localization Using Convolutional Networks
Jonathan Tompson, Ross Goroshin, Arjun Jain, Yann LeCun, Christoph Bregler
End-to-End Integration of a Convolution Network, Deformable Parts Model and Non-Maximum Suppression
Li Wan, David Eigen, Rob Fergus
Computing the Stereo Matching Cost With a Convolutional Neural Network
Jure Žbontar, Yann LeCun
Efficient and Accurate Approximations of Nonlinear Convolutional Networks
Xiangyu Zhang, Jianhua Zou, Xiang Ming, Kaiming He, Jian Sun
Deep Visual-Semantic Alignments for Generating Image Descriptions
Andrej Karpathy, Li Fei-Fei
Long-Term Recurrent Convolutional Networks for Visual Recognition and Description
Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor Darrell
Fully Convolutional Networks for Semantic Segmentation
Jonathan Long, Evan Shelhamer, Trevor Darrell
Deep Multiple Instance Learning for Image Classification and Auto-Annotation
Jiajun Wu, Yinan Yu, Chang Huang, Kai Yu
Understanding Deep Image Representations by Inverting Them
Aravindh Mahendran, Andrea Vedaldi
Convolutional Neural Networks at Constrained Time Cost
Kaiming He, Jian Sun
3D
DynamicFusion: Reconstruction and Tracking of Non-Rigid Scenes in Real-Time
Richard A. Newcombe, Dieter Fox, Steven M. Seitz
3D Scanning Deformable Objects With a Single RGBD Sensor
Mingsong Dou, Jonathan Taylor, Henry Fuchs, Andrew Fitzgibbon, Shahram Izadi
Direction Matters: Depth Estimation With a Surface Normal Classifier
Christian Häne, Ľubor Ladický, Marc Pollefeys
Designing Deep Networks for Surface Normal Estimation
Xiaolong Wang, David Fouhey, Abhinav Gupta
PAIGE: PAirwise Image Geometry Encoding for Improved Efficiency in Structure-From-Motion
Johannes L. Schönberger, Alexander C. Berg, Jan-Michael Frahm
Category-Specific Object Reconstruction From a Single Image
Abhishek Kar, Shubham Tulsiani, João Carreira, Jitendra Malik
Computing the Stereo Matching Cost With a Convolutional Neural Network
Jure Žbontar, Yann LeCun
Robust Large Scale Monocular Visual SLAM
Guillaume Bourmaud, Rémi Mégret
Reconstructing the World* in Six Days *(As Captured by the Yahoo 100 Million Image Dataset)
Jared Heinly, Johannes L. Schönberger, Enrique Dunn, Jan-Michael Frahm
Inferring 3D Layout of Building Facades From a Single Image
Jiyan Pan, Martial Hebert, Takeo Kanade
Exact Bias Correction and Covariance Estimation for Stereo Vision
Charles Freundlich, Michael Zavlanos, Philippos Mordohai
Deep Convolutional Neural Fields for Depth Estimation From a Single Image
Fayao Liu, Chunhua Shen, Guosheng Lin
Hash
Web Scale Photo Hash Clustering on A Single Machine
Yunchao Gong, Marcin Pawlowski, Fei Yang, Louis Brandy, Lubomir Bourdev, Rob Fergus
Detecion
Expanding Object Detector's Horizon: Incremental Learning Framework for Object Detection in Videos
Alina Kuznetsova, Sung Ju Hwang, Bodo Rosenhahn, Leonid Sigal
Deformable Part Models are Convolutional Neural Networks
Ross Girshick, Forrest Iandola, Trevor Darrell, Jitendra Malik
Efficient Object Localization Using Convolutional Networks
Jonathan Tompson, Ross Goroshin, Arjun Jain, Yann LeCun, Christoph Bregler
End-to-End Integration of a Convolution Network, Deformable Parts Model and Non-Maximum Suppression
Li Wan, David Eigen, Rob Fergus
Unsupervised Object Discovery and Localization in the Wild: Part-Based Matching With Bottom-Up Region Proposals
Minsu Cho, Suha Kwak, Cordelia Schmid, Jean Ponce
Model Recommendation: Generating Object Detectors From Few Samples
Yu-Xiong Wang, Martial Hebert
Learning Scene-Specific Pedestrian Detectors Without Real Data
Hironori Hattori, Vishnu Naresh Boddeti, Kris M. Kitani, Takeo Kanade
Classification
What do 15,000 Object Categories Tell Us About Classifying and Localizing Actions?
Mihir Jain, Jan C. van Gemert, Cees G. M. Snoek
From Categories to Subcategories: Large-Scale Image Classification With Partial Class Label Refinement
Marko Ristin, Juergen Gall, Matthieu Guillaumin, Luc Van Gool
Global Refinement of Random Forest
Shaoqing Ren, Xudong Cao, Yichen Wei, Jian Sun
A Novel Locally Linear KNN Model for Visual Recognition
Qingfeng Liu, Chengjun Liu
Learning From Massive Noisy Labeled Data for Image Classification
Tong Xiao, Tian Xia, Yi Yang, Chang Huang, Xiaogang Wang
Visual Recognition by Learning From Web Data: A Weakly Supervised Domain Generalization Approach
Li Niu, Wen Li, Dong Xu
Optimization&Learning
Graph-Based Simplex Method for Pairwise Energy Minimization With Binary Variables
Daniel Průša
Maximum Persistency via Iterative Relaxed Inference With Graphical Models
Alexander Shekhovtsov, Paul Swoboda, Bogdan Savchynskyy
Efficient Parallel Optimization for Potts Energy With Hierarchical Fusion
Olga Veksler
Global Supervised Descent Method
Xuehan Xiong, Fernando De la Torre
A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs With a Costly Max-Oracle
Neel Shah, Vladimir Kolmogorov, Christoph H. Lampert
Three Viewpoints Toward Exemplar SVM
Takumi Kobayashi
Iteratively Reweighted Graph Cut for Multi-Label MRFs With Non-Convex Priors
Thalaiyasingam Ajanthan, Richard Hartley, Mathieu Salzmann, Hongdong Li
Segmentation&Superpixel
Superpixel Segmentation Using Linear Spectral Clustering
Zhengqin Li, Jiansheng Chen
Real-Time Coarse-to-Fine Topologically Preserving Segmentation
Jian Yao, Marko Boben, Sanja Fidler, Raquel Urtasun
Learning to Segment Moving Objects in Videos
Katerina Fragkiadaki, Pablo Arbeláez, Panna Felsen, Jitendra Malik
Face
Web-Scale Training for Face Identification
Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf
Low-level
Image Partitioning Into Convex Polygons
Liuyun Duan, Florent Lafarge
Fast and Accurate Image Upscaling With Super-Resolution Forests
Samuel Schulter, Christian Leistner, Horst Bischof
L0TV: A New Method for Image Restoration in the Presence of Impulse Noise
Ganzhao Yuan, Bernard Ghanem
Robust Image Filtering Using Joint Static and Dynamic Guidance
Bumsub Ham, Minsu Cho, Jean Ponce
Dataset
A Large-Scale Car Dataset for Fine-Grained Categorization and Verification
Linjie Yang, Ping Luo, Chen Change Loy, Xiaoou Tang
cvpr2015总结的更多相关文章
- 论文阅读(Xiang Bai——【CVPR2015】Symmetry-Based Text Line Detection in Natural Scenes)
Xiang Bai--[CVPR2015]Symmetry-Based Text Line Detection in Natural Scenes 目录 作者和相关链接 方法概括 创新点和贡献 方法细 ...
- CVPR2015一些文章整理
简单看了一部分CVPR2015的文章.整理了一下. 当中我决定把精彩的文章加粗. 主要是认为有些文章仅仅读了一遍,没有发现非常多非常有道理的point(虽然我承认他们的工作都花了非常大的功夫.可是没有 ...
- CVPR2015深度学习回顾
原文链接:http://www.csdn.net/article/2015-08-06/2825395 本文做了少量修改,仅作转载存贮,如有疑问或版权问题,请访问原作者或告知本人. CVPR可谓计算机 ...
- 深度学习2015年文章整理(CVPR2015)
国内外从事计算机视觉和图像处理相关领域的著名学者都以在三大顶级会议(ICCV.CVPR和ECCV)上发表论文为荣,其影响力远胜于一般SCI期刊论文.这三大顶级学术会议论文也引领着未来的研究趋势.CVP ...
- CVPR2015文章下载
http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Liu_Real-Time_Part-Based_Visual_2015_ ...
- [CVPR2015] Is object localization for free? – Weakly-supervised learning with convolutional neural networks论文笔记
p.p1 { margin: 0.0px 0.0px 0.0px 0.0px; font: 13.0px "Helvetica Neue"; color: #323333 } p. ...
- 论文阅读笔记五十五:DenseBox: Unifying Landmark Localization with End to End Object Detection(CVPR2015)
论文原址:https://arxiv.org/abs/1509.04874 github:https://github.com/CaptainEven/DenseBox 摘要 本文先提出了一个问题:如 ...
- 论文阅读笔记二十八:You Only Look Once: Unified,Real-Time Object Detection(YOLO v1 CVPR2015)
论文源址:https://arxiv.org/abs/1506.02640 tensorflow代码:https://github.com/nilboy/tensorflow-yolo 摘要 该文提出 ...
- 论文阅读笔记十四:Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation(CVPR2015)
论文链接:https://arxiv.org/abs/1506.04924 摘要 该文提出了基于混合标签的半监督分割网络.与当前基于区域分类的单任务的分割方法不同,Decoupled 网络将分割与分类 ...
- 论文阅读笔记六:FCN:Fully Convolutional Networks for Semantic Segmentation(CVPR2015)
今天来看一看一个比较经典的语义分割网络,那就是FCN,全称如题,原英文论文网址:https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn ...
随机推荐
- python_docx制作word文档详细使用说明【转】
目前网上对这一个库的介绍得很少,很零散,所以很多功能我是尽量参考其官网,但是官网上面很多功能目前只有说明文档,而代码并还没有及时更新,以至于按照官网上面做了,python却报错.比如:自定义表格的 ...
- [Jmeter] Jmeter Plugins
Plugins: Plugins Manager: https://jmeter-plugins.org/wiki/PluginsManager/ Custom Thread Groups: http ...
- activemq , redis
activemq是干什么的?即时消息通信,简单说: A发送消息给activemq 服务,B监听服务获取消息.假如有如下场景: A发送了一个请求,但是这个请求需要做 10 项工作,如果按照正常操作,需要 ...
- Java SE学习【三】——JDBC
最近学到了数据库与java的jdbc方面,还有个DAO模式,写一下自己的理解,后期有什么不对的再改. 一.数据库三范式的理解 记得以前上课时,也上了一学期的“数据库系统原理”,给我们上课的老师算是渣渣 ...
- ContactDetail 和 ContactEditor 界面头像响应点击过程
1,联系人详情界面 ContactDetailFragment中处理,ViewAdapter装载数据显示头像 private final class ViewAdapter extends BaseA ...
- Redis (非关系型数据库) 数据类型 之 list列表类型
Redis列表是简单的字符串列表,按照插入顺序排序.你可以添加一个元素到列表的头部(左边)或者尾部(右边) list即可以作为“栈”也可以作为"队列". 操作: >lpush ...
- json(原生态)
什么是 JSON ? JSON 指的是 JavaScript 对象表示法(JavaScript Object Notation) JSON 是轻量级的文本数据交换格式 JSON 独立于语言 * JSO ...
- 2018.11.02 NOIP模拟 距离(斜率优化dp)
传送门 分四个方向分别讨论. 每次枚举当前行iii,然后对于第二维jjj用斜率优化dpdpdp. f[i][j]=(j−k)2+mindisk2f[i][j]=(j-k)^2+mindis_k^2f[ ...
- Java中各类Cache机制实现解决方案[来自CSDN]
摘要:在Java中,不同的类都有自己单独的Cache机制,实现的方法也可能有所不同,文章列举了Java中常见的各类Cache机制的实现方法,同时进行了综合的比较. 在Java中,不同的类都有自己单独的 ...
- 微信小程序请求数据
微信小程序请求数据,在页面展示,可以在onLoad生命周期中进行请求. 1.新建目录http,新建文件http.js 2.在js文件中暴露需要使用的变量 var baseUrl = 'http://1 ...