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awesome-computer-vision

A curated list of awesome computer vision resources.
https://github.com/wpsliu123/awesome-computer-vision

Last synced: 6 days ago
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Licenses
- ![CC0
Software
Contributing
- pull requests
Books
- Computer Vision: Models, Learning, and Inference - Simon J. D. Prince 2012
- Computer Vision: Theory and Application - Rick Szeliski 2010
- Visual Object Recognition synthesis lecture - Kristen Grauman and Bastian Leibe 2011
- Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics - Justin Solomon 2015
- Pattern Recognition and Machine Learning - Christopher M. Bishop 2007
- Neural Networks for Pattern Recognition - Christopher M. Bishop 1995
- Probabilistic Graphical Models: Principles and Techniques - Daphne Koller and Nir Friedman 2009
- Neural Networks and Deep Learning - Michael Nielsen 2014
- Pattern Recognition and Machine Learning - Christopher M. Bishop 2007
Courses
- EENG 512 / CSCI 512 - Computer Vision - William Hoff (Colorado School of Mines)
- Visual Object and Activity Recognition - Alexei A. Efros and Trevor Darrell (UC Berkeley)
- Language and Vision - Tamara Berg (UNC Chapel Hill)
- Convolutional Neural Networks for Visual Recognition - Fei-Fei Li and Andrej Karpathy (Stanford University)
- Computer Vision - Derek Hoiem (UIUC)
- Computer Vision: Foundations and Applications - Kalanit Grill-Spector and Fei-Fei Li (Stanford University)
- High-Level Vision: Behaviors, Neurons and Computational Models - Fei-Fei Li (Stanford University)
- Advances in Computer Vision - Antonio Torralba and Bill Freeman (MIT)
- Computer Vision - Bastian Leibe (RWTH Aachen University)
- Computer Vision 2 - Bastian Leibe (RWTH Aachen University)
- Computer Vision
- Computer Vision 1
- Computer Vision 2
- Computational Photography - Alexei A. Efros (CMU)
- Digital & Computational Photography - Fredo Durand (MIT)
- Courses in Graphics - Stanford University
- Introduction to Visual Computing - Kyros Kutulakos (University of Toronto)
- Computational Photography - Kyros Kutulakos (University of Toronto)
- Machine Learning - Andrew Ng (Stanford University)
- Statistical Learning - Trevor Hastie and Rob Tibshirani (Stanford University)
- Statistical Learning Theory and Applications - Tomaso Poggio, Lorenzo Rosasco, Carlo Ciliberto, Charlie Frogner, Georgios Evangelopoulos, Ben Deen (MIT)
- Statistical Learning - Genevera Allen (Rice University)
- Course on Information Theory, Pattern Recognition, and Neural Networks - David MacKay (University of Cambridge)
- Methods for Applied Statistics: Unsupervised Learning - Lester Mackey (Stanford)
- Intro to Machine Learning - Sebastian Thrun (Stanford University)
- (Convolutional) Neural Networks for Visual Recognition - Fei-Fei Li, Andrej Karphaty, Justin Johnson (Stanford University)
- Machine Learning for Computer Vision - Rudolph Triebel (TU Munich)
- Convex Optimization II - Stephen Boyd (Stanford University)
- Convex Optimization - Stephen Boyd (Stanford University)
- Optimization at MIT - (MIT)
- Convex Optimization - Ryan Tibshirani (CMU)
- Image Manipulation and Computational Photography - Alexei A. Efros (UC Berkeley)
- Computational Photography - James Hays (Brown University)
- Computational Photography - Derek Hoiem (UIUC)
- Learning from Data - Yaser S. Abu-Mostafa (Caltech)
- Multiple View Geometry
- Machine Learning for Computer Vision - Rudolph Triebel (TU Munich)
Papers
- CVPapers - Computer vision papers on the web
- Visionbib Survey Paper List
- Computer Vision: A Reference Guide
Tutorials and talks
- 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
- ECCV 2014 - Sep 2014
- ECCV 2012 - Oct 2012
- 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
- 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
- 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
- Computational Photography - William T. Freeman (MIT) 2012
- Overview of Computer Vision and Visual Effects - Rich Radke (Rensselaer Polytechnic Institute) 2014
- Learning in Computer Vision - Simon Lucey (CMU) 2008
- Object Recognition - Larry Zitnick (Microsoft Research)
- Graphical Models - Zoubin Ghahramani (University of Cambridge) 2009
- Machine Learning, Probability and Graphical Models - Sam Roweis (NYU) 2006
- 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
- Optimization Algorithms in Machine Learning - Stephen J. Wright (University of Wisconsin-Madison)
- Continuous Optimization in Computer Vision - Andrew Fitzgibbon (Microsoft Research)
- Beyond stochastic gradient descent for large-scale machine learning - Francis Bach (INRIA)
- A tutorial on Deep Learning - Geoffrey E. Hinton (University of Toronto)
- Scaling up Deep Learning - Yoshua Bengio (University of Montreal)
- The Unreasonable Effectivness Of Deep Learning
- 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
- 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)
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Bayesian or Frequentist, Which Are You? - Michael I. Jordan (UC Berkeley)
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Revealing the Invisible - Frédo Durand (MIT) 2012
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Object Recognition - Larry Zitnick (Microsoft Research)
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
Datasets
Resources for students
- Resources for students - Frédo Durand (MIT)
- Advice for Graduate Students - Aaron Hertzmann (Adobe Research)
- Graduate Skills Seminars - Yashar Ganjali, Aaron Hertzmann (University of Toronto)
- Research Skills - Simon Peyton Jones (Microsoft Research)
- Resource collection - Tao Xie (UIUC) and Yuan Xie (UCSB)
- How to write a good CVPR submission - William T. Freeman (MIT)
- How to write a great research paper - Simon Peyton Jones (Microsoft Research)
- Writing Research Papers - Aaron Hertzmann (Adobe Research)
- How to Write a Great Paper - Martin Martin Hering Hering--Bertram (Hochschule Bremen University of Applied Sciences)
- How to have a paper get into SIGGRAPH? - Takeo Igarashi (The University of Tokyo)
- Good Writing - Marc H. Raibert (Boston Dynamics, Inc.)
- How to Write a Computer Vision Paper - Derek Hoiem (UIUC)
- Common mistakes in technical writing - Wojciech Jarosz (Dartmouth College)
- How to give a good talk - David Fleet (University of Toronto) and Aaron Hertzmann (Adobe Research)
- How to do research - William T. Freeman (MIT)
- Warning Signs of Bogus Progress in Research in an Age of Rich Computation and Information - Yi Ma (UIUC)
- Five Principles for Choosing Research Problems in Computer Graphics - Thomas Funkhouser (Cornell University)
- How To Do Research In the MIT AI Lab - David Chapman (MIT)
- Time Management - Randy Pausch (CMU)
- Write Good Papers - Frédo Durand (MIT)
- Giving a Research Talk - Frédo Durand (MIT)
- Notes on writing - Frédo Durand (MIT)
- How to Write a Bad Article - Frédo Durand (MIT)
Blogs
- Andrej Karpathy blog - Andrej Karpathy
Links
Songs

Programming Languages

Categories

Software 226 Tutorials and talks 53 Datasets 46 Courses 37 Resources for students 23 Books 9 Links 4 Papers 3 Songs 3 Licenses 1 Blogs 1 Contributing 1

Sub Categories

Keywords

recurrent-networks 1 neural-network 1 machine-learning 1 face-images 1 deep-networks 1 deep-learning-tutorial 1 deep-learning 1 awesome-list 1 awesome 1