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. A面&B面

    难难难.道是玄,不遇知音不可谈.遇了知音聊两句,免教那枉费舌尖.难得今天心情不错,反思毕业这五年的种种,有浑噩.迷茫.彷徨.莽撞.执着.困顿.不惧,走到今天迈过了几道坎早已忘却,同时也还在询问自己值不 ...

  2. mybatis 复杂传参

    1基本传参数 Public User selectUserWithCon(@param(“userName”)String  name,@param(“userArea”)String area); ...

  3. 解决maven update project 后项目jdk变成1.5的问题

    一.问题描述 在Eclipse中新建了一个Maven工程, 然后更改JDK版本为1.7, 结果每次使用Maven > Update project的时候JDK版本都恢复成1.5. 二.原因分析 ...

  4. SpringMVC学习笔记:表单提交 参数的接收

    SpringMVC可以接收原生form表单和json格式数据 有一个名为Book的model,其中的属性如下: 字符串类型的name,数字类型的price,数组类型的cover,集合类型的author ...

  5. C语言实现用位移运算符进行加减乘…

      最近,在百度知道上回答问题,然后看见有的人问如何用位移运算符去进行加减乘除运算,于是巩固今天就在这总结一下.   先讲讲总体思路: 加法运算:将一个整数用二进制表示,其加法运算就是:相异(^)时, ...

  6. canvas 实现贪吃蛇游戏

    var canvas = document.getElementById('canvas'); var cxt = canvas.getContext('2d'); // 定时器 var timer; ...

  7. python学习 day1 (3月1日)

    01 cpu 内存 硬盘 操作系统 CPU:中央处理器,相当于人大脑. 飞机 内存:临时存储数据. 8g,16g, 高铁 1,成本高. 2,断电即消失. 硬盘:长期存储大量的数据. 1T 512G等等 ...

  8. 进入快速通道的委托(深入理解c#)

    1.方法组:所有的名称相同的重载方法合在一起就成为一个方法组. 2.协变性和逆变性: 协变性指的是——泛型类型参数可以从一个派生类隐式转化为基类. 逆变性指的是——泛型类型参数可以从一个基类隐式转化为 ...

  9. django之补充

    一 QuerySet类型 QuerySet类型:只和orm有关,如果一涉及数据库,就会有QuerySet类型的出现. QuerySet切片操作:QuerySet是支持切片操作的,不过不能放负数.查询集 ...

  10. thinkphp5 数据库和模型

    1.Db和模型的存在只是ThinkPHP5.0架构设计中的职责和定位不同,Db负责的只是数据(表)访问,模型负责的是业务数据和业务逻辑.2.Db和模型最明显的一个区别就是Db查询返回的数据类型为数组( ...