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  • CVPR历年Best Papers

    cvpr

    作者:我爱机器学习
    原文链接:CVPR历年Best Papers

    CVPR (Computer Vision)(2000-2016)
    年份 标题 一作 一作单位
    2016 Deep Residual Learning for Image Recognition Kaiming He Microsoft Research
    2015 DynamicFusion: Reconstruction and Tracking of Non-rigid Scenes in Real-Time Richard A. Newcombe University of Washington
    2014 What Camera Motion Reveals About Shape with Unknown BRDF Manmohan Chandraker NEC Labs America
    2013 Fast, Accurate Detection of 100,000 Object Classes on a Single Machine Thomas Dean Google
    2012 A Simple Prior-free Method for Non-Rigid Structure-from-Motion Factorization Yuchao Dai Northwestern Polytechnical University
    2011 Real-time Human Pose Recognition in Parts from Single Depth Images Jamie Shotton Microsoft Research
    2010 Efficient Computation of Robust Low-Rank Matrix Approximations in the Presence of Missing Data usi… Anders Eriksson University of Adelaide
    2009 Single Image Haze Removal Using Dark Channel Prior Kaiming He The Chinese University of Hong Kong
    2008 Global Stereo Reconstruction under Second Order Smoothness Priors Oliver Woodford University of Oxford
    Beyond Sliding Windows: Object Localization by Efficient Subwindow Search Chistoph H. Lampert Max Planck Institut
    2007 Dynamic 3D Scene Analysis from a Moving Vehicle Bastian Leibe ETH Zurich
    2006 Putting Objects in Perspective Derek Hoiem Carnegie Mellon University
    2005 Real-Time Non-Rigid Surface Detection Julien Pilet École Polytechnique Fédérale de Lausanne
    2004 Programmable Imaging using a Digital Micromirror Array Shree K. Nayar Columbia University
    2003 Object Class Recognition by Unsupervised Scale-Invariant Learning Rob Fergus University of Oxford
    2001 Morphable 3D models from video Matthew Brand Mitsubishi Electric Research Laboratories
    2000 Real-Time Tracking of Non-Rigid Objects using Mean Shift Dorin Comaniciu Siemens Corporate Research

    参考文献:Best Paper Awards in Computer Science (since 1996)

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  • 原文地址:https://www.cnblogs.com/52machinelearning/p/5821581.html
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