摘录ECCV2016部分文章,主要有Human pose esimation,  Human activiity / actions, Face alignment, Face detection & recognition & .. , Hand tracking, Eye, and Others.

以下为文章及标题(可能有错漏)

Human pose estimation:

[1]Towards Viewpoint Invariant 3DHuman Pose Estimation

Albert Haque, Boya Peng, Zelun Luo, Alexandre Alahi, Serena Yeung,and Li Fei-Fei

[2]Fast 6D Pose Estimation from aMonocular Image UsingHierarchical Pose Trees

Yoshinori Konishi, Yuki Hanzawa, Masato Kawade,and Manabu Hashimoto

[3]Keep It SMPL: AutomaticEstimation of 3D Human Pose and Shapefrom a SingleImage

Federica Bogo, Angjoo Kanazawa, Christoph Lassner, Peter Gehler,Javier Romero, and Michael J. Black

[4] Zoom Better to See Clearer: Human and Object Parsing withHierarchicalAuto-Zoom Net

Fangting Xia, PengWang, Liang-Chieh Chen, and Alan L. Yuille

[5] A Sequential Approach to 3D Human Pose Estimation: Separationof Localization and Identification of Body Joints

Ho Yub Jung, YuminSuh, Gyeongsik Moon, and Kyoung Mu Lee

[6]DeeperCut: A Deeper, Stronger,and Faster Multi-person PoseEstimation Model

Eldar Insafutdinov, Leonid Pishchulin, Bjoern Andres,Mykhaylo Andriluka, and Bernt Schiele

[7]Human Attribute Recognition byDeep Hierarchical Contexts

Yining Li, Chen Huang, Chen Change Loy, and Xiaoou Tang

[8]Human Pose Estimation UsingDeep Consensus Voting .

Ita Lifshitz, Ethan Fetaya, and Shimon Ullman

[9]Human Pose Estimation viaConvolutional Part Heatmap Regression

Adrian Bulat and Georgios Tzimiropoulos

[10]Stacked Hourglass Networks forHuman Pose Estimation

Alejandro Newell, Kaiyu Yang, and Jia Deng

[11]Bayesian Image Based 3D PoseEstimation

Marta Sanzari, Valsamis Ntouskos, and Fiora Pirri

[12]Shape from Selfies: Human BodyShape Estimation Using CCARegression Forests

Endri Dibra, Cengiz Öztireli, Remo Ziegler, and Markus Gross

[13]Estimation of Human Body Shapein Motion with Wide Clothing

Jinlong Yang, Jean-Sébastien Franco, Franck Hétroy-Wheeler,and Stefanie Wuhrer

[14]Chained Predictions UsingConvolutional Neural Networks

Georgia Gkioxari, Alexander Toshev, and Navdeep Jaitly

Human activity:

[1]Real-Time RGB-D ActivityPrediction by Soft Regression

Jian-Fang Hu, Wei-ShiZheng, Lianyang Ma, Gang Wang,and Jianhuang Lai

[2]Learning Models for Actionsand Person-Object Interactions with Transferto QuestionAnswering

Arun Mallya and Svetlana Lazebnik

[3]RNN Fisher Vectors for ActionRecognition and Image Annotation.

Guy Lev, Gil Sadeh, Benjamin Klein, and Lior Wolf

[4]Online Human Action DetectionUsing Joint Classification-RegressionRecurrent NeuralNetworks

Yanghao Li, Cuiling Lan, Junliang Xing, Wenjun Zeng, Chunfeng Yuan,and Jiaying Liu

[5]DAPs: Deep Action Proposalsfor Action Understanding

Victor Escorcia, Fabian Caba Heilbron, Juan Carlos Niebles,and Bernard Ghanem

[6]Spatio-Temporal LSTM withTrust Gates for 3D HumanAction Recognition

Jun Liu, Amir Shahroudy, Dong Xu, and Gang Wang

[7]Multi-region Two-Stream R-CNNfor Action Detection

Xiaojiang Peng and Cordelia Schmid

Face alignment:

[1]A Recurrent Encoder-DecoderNetwork for Sequential Face Alignment

Xi Peng, Rogerio S. Feris, Xiaoyu Wang, and Dimitris N. Metaxas

[2]Robust Facial LandmarkDetection via Recurrent Attentive-RefinementNetworks

Shengtao Xiao, Jiashi Feng, Junliang Xing, Hanjiang Lai,Shuicheng Yan, and Ashraf Kassim

[3]Deep Deformation Network forObject Landmark Localization

Xiang Yu, Feng Zhou, and ManmohanChandraker

[4]Joint Face Alignment and 3DFace Reconstruction

Feng Liu, Dan Zeng, Qijun Zhao, and Xiaoming Liu

[5]Robust Face Alignment Using aMixture of Invariant Experts

Oncel Tuzel, Tim K. Marks, and Salil Tambe

Face detection & recognition& …:

[1]MOON: A Mixed Objective Optimization Network for the Recognitionof Facial Attributes

Ethan M. Rudd, Manuel Günther, and Terrance E. Boult

[2]Supervised Transformer Networkfor Efficient Face Detection

Dong Chen, Gang Hua,Fang Wen, and Jian Sun

[3]Ultra-Resolving Face Images byDiscriminative Generative Networks

Xin Yu and Fatih Porikli

[4]Do We Really Need to CollectMillions of Faces for EffectiveFace Recognition?

Iacopo Masi, Anh Tuấn Trần, Tal Hassner,Jatuporn Toy Leksut,and Gérard Medioni

[5]Deep Cascaded Bi-Network forFace Hallucination

Shizhan Zhu, SifeiLiu, Chen Change Loy, and Xiaoou Tang

[6]Real-Time Facial Segmentationand Performance Capture from RGB Input

Shunsuke Saito, Tianye Li, and Hao Li

[7]Cascaded Continuous Regressionfor Real-Time Incremental Face Tracking

Enrique Sánchez-Lozano, Brais Martinez, Georgios Tzimiropoulos,and Michel Valstar

[8]MS-Celeb-1M: A Dataset andBenchmark for Large-ScaleFace Recognition

Yandong Guo, LeiZhang, Yuxiao Hu, Xiaodong He, and Jianfeng Gao

[9]Joint Face RepresentationAdaptation and Clustering in Videos.

Zhanpeng Zhang, Ping Luo, Chen Change Loy, and Xiaoou Tang

[10]Grid Loss: Detecting OccludedFaces

Michael Opitz, Georg Waltner, Georg Poier, Horst Possegger,and Horst Bischof

[11]Face Detection with End-to-EndIntegration of a ConvNet and a 3D Model

Yunzhu Li, BenyuanSun, Tianfu Wu, and Yizhou Wang

[12]Face Recognition from MultipleStylistic Sketches: Scenarios, Datasets,and Evaluation

Chunlei Peng,Nannan Wang, Xinbo Gao, and Jie Li

[13]Fast Face Sketch Synthesis viaKD-Tree Search

Yuqian Zhang,Nannan Wang, Shengchuan Zhang, Jie Li,and Xinbo Gao

Eye:

[1]A 3D Morphable Eye RegionModel for Gaze Estimation

Erroll Wood, Tadas Baltrušaitis, Louis-Philippe Morency,Peter Robinson, and Andreas Bulling

Hand:

[1]Real-Time Joint Tracking of aHand Manipulating an Objectfrom RGB-D Input

Srinath Sridhar, Franziska Mueller, Michael Zollhöfer, Dan Casas,Antti Oulasvirta, and Christian Theobalt

[2]Spatial Attention Deep Netwith Partial PSO for Hierarchical HybridHand PoseEstimation

Qi Ye, Shanxin Yuan, and Tae-Kyun Kim

[3]Hand Pose Estimation fromLocal Surface Normals

Chengde Wan, AngelaYao, and Luc Van Gool

Others:

[1]DOC: Deep OCclusion Estimationfrom a Single Image.

Peng Wang and AlanYuille

[2]Convolutional OrientedBoundaries

Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Pablo Arbeláez,and Luc Van Gool

[3]Superpixel ConvolutionalNetworks Using Bilateral Inceptions

Raghudeep Gadde, VarunJampani, Martin Kiefel, Daniel Kappler,and Peter V.Gehler

[4]SDF-2-SDF: Highly Accurate 3DObject Reconstruction

Miroslava Slavcheva,Wadim Kehl, Nassir Navab, and Slobodan Ilic

[5]Learning to Hash with BinaryDeep Neural Network

Thanh-Toan Do,Anh-Dzung Doan, and Ngai-Man Cheung

[6]Going Further with Point PairFeatures

Stefan Hinterstoisser, Vincent Lepetit, Naresh Rajkumar,and Kurt Konolige

[7]Automatic Attribute Discoverywith Neural Activations

SirionVittayakorn, Takayuki Umeda, Kazuhiko Murasaki, Kyoko Sudo,Takayuki Okatani, and Kota Yamaguchi

ECCV 2016 paper list的更多相关文章

  1. Learning to Track at 100 FPS with Deep Regression Networks ECCV 2016 论文笔记

    Learning to Track at 100 FPS with Deep Regression Networks   ECCV 2016  论文笔记 工程网页:http://davheld.git ...

  2. CVPR 2016 paper reading (2)

    1. Sketch me that shoe, Qian Yu, Feng Liu, Yi-Zhe Song, Tao Xiang, Timothy M. Hospedales, Cheng Chan ...

  3. AAAI 2016 paper阅读

    本篇文章调研一些感兴趣的AAAI 2016 papers.科研要多读paper!!! Learning to Generate Posters of Scientific Papers,Yuting ...

  4. CVPR 2016 paper reading (6)

    1. Neuroaesthetics in fashion: modeling the perception of fashionability, Edgar Simo-Serra, Sanja Fi ...

  5. CVPR 2016 paper reading (3)

    DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations, Ziwei Liu, Pin ...

  6. Deep Image Retrieval: Learning global representations for image search In ECCV, 2016学习笔记

    - 论文地址:https://arxiv.org/abs/1604.01325 contribution is twofold: (i) we leverage a ranking framework ...

  7. Summary on Visual Tracking: Paper List, Benchmarks and Top Groups

    Summary on Visual Tracking: Paper List, Benchmarks and Top Groups 2018-07-26 10:32:15 This blog is c ...

  8. Ubuntu_ROS中应用kinect v2笔记

    Ubuntu_ROS中应用kinect v2笔记 个人觉得最重要的资料如下: 1. Microsoft Kinect v2 Driver Released http://www.ros.org/new ...

  9. (转)Multi-Object-Tracking-Paper-List

    Multi-Object-Tracking-Paper-List 2018-08-07 22:18:05 This blog is copied from: https://github.com/Sp ...

随机推荐

  1. Java 使用 jacob 将 word 文档转换为 pdf 文件

    网上查询了许许多多的博客,说利用 poi.iText.Jsoup.jdoctopdf.使用 jodconverter 来调用 openOffice 的服务来转换等等,我尝试了很多种,但要么显示不完全, ...

  2. 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 ...

  3. Mockito学习(zz)

    junitmaven软件测试框架项目管理  Mockito是一个流行的Mocking框架.它使用起来简单,学习成本很低,而且具有非常简洁的API,测试代码的可读性很高.因此它十分受欢迎,用 户群越来越 ...

  4. maven 介绍(zz )

    Maven 编辑     目录 1简介 2特点 3常用命令 4推荐书籍 5Win7配置 6生命周期     1   1简介 Maven是基于项目对象模型(POM),可以通过一小段描述信息来管理项目的构 ...

  5. git舍弃文件更改

    未进行任何提交,即文件更改在工作区 # filename 对应进行操作的文件名 git checkout -- filename 已用git add 命令提交,即文件更改在暂存区 # 舍弃暂存区的修改 ...

  6. 【C#】详解C#委托

    目录结构: contents structure [+] 委托语法 泛型委托 委托链 lambda表达式 揭秘委托 类库中的委托 委托和反射 1.委托语法 本文会详细阐述委托的使用,以及实现,想必读者 ...

  7. IDEA如何把写好的java文件/项目打包成一个jar的文件

    一.命令行的方法 打开cmd,输入jar -cvf [打包后的文件名].jar [要打包的目录]. 二.IDEA的方法 写完一个java程序把它封装成一个jar的包  这样就可以在别的jar上面运行这 ...

  8. java 模拟登录新浪微博(通过cookie)

    这几天一直在研究新浪微博的爬虫,发现爬取微博的数据首先要登录.本来打算是通过账号和密码模拟浏览器登录.但是现在微博的登录机制比较复杂.通过账号密码还没有登录成功QAQ.所以就先记录下,通过cookie ...

  9. 关于java项目中的XML文件

    一,xml的机制 1.xml文件会在服务器启动的时候进行加载 2.加载完成后根据xml文件里面配置的属性对集成的对象进行属性和行为赋予 3.xml会有很多不同的标签,每个标签都有它特定的含义 二.为什 ...

  10. 使用promise判断是否登录

    步骤: 1.创建并返回new Promise((success,error)=>{}),success和error分别是成功和失败后所执行的函数 2.判断是否含有cookie,如果含有cooki ...