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. Gym - 100735E Restore

    E - Restore 题意:输入一个n,输入一个对角线空缺(为0)的n*n的矩阵,要求每一行每一列和对角线的和相同,输出完整的矩阵. 解法:设每一行的和都是sum,用一个h[]数组存每一行的和.则可 ...

  2. ServiceDesk Plus解析内容,简化工单管理

  3. mysqldb mysql_config

    在安装mysqldb Python的时候会用到mysql_config,但是正常安装的MySQL环境下是没有这个文件的,这个文件在Linux下是可执行文件,所以需要到mysql官方网站上下载MySQL ...

  4. test面板1

    Ext.onReady(function(){                var myPanel=new Ext.TabPanel({                    renderTo:Ex ...

  5. Codeforces 1103 简要题解(持续更新)

    文章目录 A题 B题 C题 D题 传送门 又一场原地爆炸的比赛. A题 传送门 简单思维题 题意:给一个4∗44*44∗4的格子图和一个01串,你要根据01串放1∗21*21∗2的木块,如果是0就竖放 ...

  6. Linux 第三天

    2.文件处理命令 1)touch 创建空文件 语法:touch文件名 2)cat 显示文件内容 英文原意:concatenate 语法:cat 文件名 常用选项: -n:number,显示行号 3)t ...

  7. hdu-1394(线段树&逆序数的性质和求法)

    题目链接:http://acm.hdu.edu.cn/showproblem.php?pid=1394 题目大意: 给出一个序列,一对逆序数就是满足i<j&&a[i]>a[ ...

  8. 分分钟搞懂union与union all

    SQL UNION 操作符 UNION 操作符用于合并两个或多个 SELECT 语句的结果集. 请注意,UNION 内部的 SELECT 语句必须拥有相同数量的列.列也必须拥有相似的数据类型.同时,每 ...

  9. printk()、查看开机log、查看实时log

    要将linux内核的带级别控制的printk内容打印出来,在命令行 输入 dmesg -n 8 就将所有级别的信息都打印出来 Linux命令:dmesg 功能说明:显示开机信息. 语 法:dmesg ...

  10. HDMI之HPD

    HDMI(19Pin)/DVI(16 pin)的功能是热插拔检测(Hot Plug Detect,HPD),这个信号将作为主机系统是否对HDMI/DVI是否发送TMDS信号的依据.HPD是从显示器输出 ...