关于机器视觉，你想了解的都在这里（2）

各位学霸，机器视觉十全大补丸第二弹又来了。（酱学堂 | 关于机器视觉，你想了解的都在这里（一））不知道之前的那一溜儿教程，大家看到哪一章了？要成为一个大牛，我们可以先给自己定一个小目标，列如说，把下面提到的Paper 都看了。

OH No！！！！！

再安利下，如过你也想对这个项目做出贡献，你可以email项目的发起者Jia-Bin Huang（jbhuang1@illinois.edu）

计算机视觉十全大补丸

发起人: Jia-Bin Huang

研究方向： physically grounded visual synthesis and analysis.

Paper

会议 Paper

CVPapers – Computer vision papers on the web

SIGGRAPH Paper on the web – Graphics papers on the web

NIPS Proceedings – NIPS papers on the web

Computer Vision Foundation open access

Annotated Computer Vision Bibliography – Keith Price (USC)

Calendar of Computer Image Analysis, Computer Vision Conferences – (USC)

Survey Papers

Visionbib Survey Paper List

Foundations and Trends® in Computer Graphics and Vision

Computer Vision: A Reference Guide

演讲及Tutorial

机器视觉

Computer Vision Talks – Lectures, keynotes, panel discussions on computer vision

The Three R's of Computer Vision – Jitendra Malik (UC Berkeley) 2013

Applications to Machine Vision – Andrew Blake (Microsoft Research) 2008

The Future of Image Search – Jitendra Malik (UC Berkeley) 2008

Should I do a PhD in Computer Vision? – Fatih Porikli (Australian National University)

Graduate Summer School 2013: Computer Vision – IPAM, 2013

近期演讲

CVPR 2015 – Jun 2015

ECCV 2014 – Sep 2014

CVPR 2014 – Jun 2014

ICCV 2013 – Dec 2013

ICML 2013 – Jul 2013

CVPR 2013 – Jun 2013

ECCV 2012 – Oct 2012

ICML 2012 – Jun 2012

CVPR 2012 – Jun 2012

3D 机器视觉

3D Computer Vision: Past, Present, and Future – Steve Seitz (University of Washington) 2011

Reconstructing the World from Photos on the Internet – Steve Seitz (University of Washington) 2013

Internet Vision

The Distributed Camera – Noah Snavely (Cornell University) 2011

Planet-Scale Visual Understanding – Noah Snavely (Cornell University) 2014

A Trillion Photos – Steve Seitz (University of Washington) 2013

Computational Photography

Reflections on Image-Based Modeling and Rendering – Richard Szeliski (Microsoft Research) 2013

Photographing Events over Time – William T. Freeman (MIT) 2011

Old and New algorithm for Blind Deconvolution – Yair Weiss (The Hebrew University of Jerusalem) 2011

A Tour of Modern “Image Processing” – Peyman Milanfar (UC Santa Cruz/Google) 2010

Topics in image and video processing Andrew Blake (Microsoft Research) 2007

Computational Photography – William T. Freeman (MIT) 2012

Revealing the Invisible – Frédo Durand (MIT) 2012

Overview of Computer Vision and Visual Effects – Rich Radke (Rensselaer Polytechnic Institute) 2014

Learning and Vision

Where machine vision needs help from machine learning – William T. Freeman (MIT) 2011

Learning in Computer Vision – Simon Lucey (CMU) 2008

Learning and Inference in Low-Level Vision – Yair Weiss (The Hebrew University of Jerusalem) 2009

物体识别

Object Recognition – Larry Zitnick (Microsoft Research)

Generative Models for Visual Objects and Object Recognition via Bayesian Inference – Fei-Fei Li (Stanford University)

Graphical Models

Graphical Models for Computer Vision – Pedro Felzenszwalb (Brown University) 2012

Graphical Models – Zoubin Ghahramani (University of Cambridge) 2009

Machine Learning, Probability and Graphical Models – Sam Roweis (NYU) 2006

Graphical Models and Applications – Yair Weiss (The Hebrew University of Jerusalem) 2009

机器学习

A Gentle Tutorial of the EM Algorithm – Jeff A. Bilmes (UC Berkeley) 1998

Introduction To Bayesian Inference – Christopher Bishop (Microsoft Research) 2009

Support Vector Machines – Chih-Jen Lin (National Taiwan University) 2006

Bayesian or Frequentist, Which Are You? – Michael I. Jordan (UC Berkeley)

优化

Optimization Algorithms in Machine Learning – Stephen J. Wright (University of Wisconsin-Madison)

Convex Optimization – Lieven Vandenberghe (University of California, Los Angeles)

Continuous Optimization in Computer Vision – Andrew Fitzgibbon (Microsoft Research)

Beyond stochastic gradient descent for large-scale machine learning – Francis Bach (INRIA)

Variational Methods for Computer Vision – Daniel Cremers (Technische Universität München) (lecture 18 missing from playlist)

Deep Learning

A tutorial on Deep Learning – Geoffrey E. Hinton (University of Toronto)

Deep Learning – Ruslan Salakhutdinov (University of Toronto)

Scaling up Deep Learning – Yoshua Bengio (University of Montreal)

ImageNet Classification with Deep Convolutional Neural Networks – Alex Krizhevsky (University of Toronto)

The Unreasonable Effectivness Of Deep Learning Yann LeCun (NYU/Facebook Research) 2014

Deep Learning for Computer Vision – Rob Fergus (NYU/Facebook Research)

High-dimensional learning with deep network contractions – Stéphane Mallat (Ecole Normale Superieure)

Graduate Summer School 2012: Deep Learning, Feature Learning – IPAM, 2012

Workshop on Big Data and Statistical Machine Learning

Machine Learning Summer School – Reykjavik, Iceland 2014

Deep Learning Session 1 – Yoshua Bengio (Universtiy of Montreal)

Deep Learning Session 2 – Yoshua Bengio (University of Montreal)

Deep Learning Session 3 – Yoshua Bengio (University of Montreal)

未完待续

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