Awesome-People-in-Computer-Vision

CV researchers

A curated list of influential computer vision researchers and their contributions to the field.

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last commit: about 7 years ago
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Awesome People in Computer Vision
Computer Vision Genealogy Project and
The Mathematics Geneology Project
William T. Freeman (MIT)

Awesome People in Computer Vision / William T. Freeman
Hyun Sung Chang
Taeg Sang ("Tim") Cho (Wilmer Hale)
Roger Grosse (University of Toronto)
Biliana Kaneva (MIT)
Ce Liu (Google Research)
Michael Rubinstein (Google Research)
Bryan Russell (Adobe Research)
Barun Singh
Erik B. Sudderth (Brown University)
Marshall Tappen (Amazon)

Awesome People in Computer Vision
Eero Simoncelli (NYU)
Yair Weiss (The Hebrew University of Jerusalem)

Awesome People in Computer Vision / Yair Weiss
Anat Levin (The Weizmann Institute of Science)

Awesome People in Computer Vision
Ron Dror (Stanford University)
Roland Fleming (University of Giessen)
Marshall Tappen (Amazon)
Josh McDermott (MIT)
Yuanzhen Li
Lavanya Sharan (MIT)
Ce Liu (Google Research)
Nathaniel Twarog
Rodney Brooks (MIT)
David Lowe (University of British Columbia)
David Kriegman (UCSD)
Jitendra Malik (UC Berkeley)

Awesome People in Computer Vision / Jitendra Malik
Paul Kube (UCSD)
Pietro Perona (Caltech)
Clark F. Olson (University of Washington, Bothell)
Ruth Rosenholtz (MIT)
Paul Debevec (USC)
Christoph Bregler (Facebook/Oculus)
Jianbo Shi (University of Pennsylvania)
Yizhou Yu (The University of Hong Kong)
Serge J. Belongie (Cornell Tech)

Awesome People in Computer Vision / Jitendra Malik / Serge J. Belongie
Piotr Dollár (Facebook AI Research)
Tsung-Yi Lin (Cornell Tech)

Awesome People in Computer Vision / Jitendra Malik
Alexei A. Efros (UC Berkeley)

Awesome People in Computer Vision / Jitendra Malik / Alexei A. Efros
Derek Hoiem (UIUC)
James Hays (Brown University)
Jean-François Lalonde (Laval University)
Tomasz Malisiewicz (VISION.AI, LLC)
Santosh Divvala (Allen Institute for Artificial Intelligence)

Awesome People in Computer Vision / Jitendra Malik
Greg Mori (Simon Fraser University)
Charless C. Fowlkes (UC Irvine)
Arie E. Kaufman (Stonybrook University)
Xiaofeng Ren (Amazon)
Subhransu Maji (University of Massachusetts, Amherst)
Lubomir Bourdev (Facebook AI Research)

Awesome People in Computer Vision
Michael Kass (Pixar Research Group)
Sheng-Jyh Wang (National Chiao Tung University)
Mohamed Abdel-Mottaleb (University of Miami)
Narendra Ahuja (UIUC)

Awesome People in Computer Vision / Narendra Ahuja
Ming-Hsuan Yang (UC Merced)

Awesome People in Computer Vision
Rama Chellappa (University of Maryland)
Larry S. Davis (University of Maryland)
Sven Dickinson (University of Toronto)
David S. Doermann (University of Maryland)
Zoran Duric (George Mason University)
Charles R. Dyer (University of Wisconsin - Madison)

Awesome People in Computer Vision / Charles R. Dyer
Steven Seitz (University of Washington)

Awesome People in Computer Vision / Charles R. Dyer / Steven Seitz
Noah Snavely (Cornell University/Google)
Li Zhang (Google)
Jovan Popović (Adobe Research)
Daniel Goldman (Adobe)

Awesome People in Computer Vision
Jan-Olof Eklundh (KTH - Royal Institute of Technology)
Shmuel Peleg (The Hebrew University of Jerusalem)

Awesome People in Computer Vision / Shmuel Peleg
Haim Schweitzer (The University of Texas at Dallas)
Michael Werman (The Hebrew University of Jerusalem)
Daniel Keren (Haifa University)
Yacov Hel-Or (The Interdisciplinary Center)
Michal Irani (The Weizmann Institute of Science)

Awesome People in Computer Vision / Shmuel Peleg / Michal Irani
Eli Shechtman (Adobe Research)

Awesome People in Computer Vision / Shmuel Peleg
Hagit Zabrodsky (Hel-Or) (University of Haifa)
Moshe Ben-Ezra (MIT Media Lab)
Yael Pritch (Google [X])

Awesome People in Computer Vision
Matti Pietikäinen (University of Oulu)
Zygmunt Pizlo (Purdue University)
Zhengyou Zhang (Microsoft Research)
Pascal Fua (EPFL)
Martial Hebert (CMU)

Awesome People in Computer Vision / Martial Hebert
Andrew Johnson (JPL)

Awesome People in Computer Vision
Michael Leventon (D. E. Shaw & Co.)
Jonathan Touboul (Collège de France and INRIA Paris-Rocquencourt)
Jean Ponce (Ecole Normale Supérieure)

Awesome People in Computer Vision / Jean Ponce
Yasutaka Furukawa (Washington University in St. Louis)
Oliver Whyte (Microsoft)
Y-Lan Boureau (NYU)
Olivier Duchenne (Inter Korea)
Armand Joulin (Facebook AI Research)
Svetlana Lazebnik (UIUC)

Awesome People in Computer Vision / Jean Ponce / Svetlana Lazebnik
Joseph Tighe (Amazon)
Yunchao Gong (Facebook AI Research)

Awesome People in Computer Vision
Peter N. Belhumeur (Columbia University/Dropbox)

Awesome People in Computer Vision / Peter N. Belhumeur
Todd Zickler (Harvard University)

Awesome People in Computer Vision / Peter N. Belhumeur / Todd Zickler
Ayan Chakrabarti (Toyota Technological Institute at Chicago)
Sanjeev Koppal (University of Florida)

Awesome People in Computer Vision / Peter N. Belhumeur
Neeraj Kumar (Dropbox)

Awesome People in Computer Vision
Trevor Darrell (UC Berkeley)

Awesome People in Computer Vision / Trevor Darrell
Gregory Shakhnarovich (Toyota Technological Institute at Chicago)
Kristen Grauman (UT Austin)

Awesome People in Computer Vision / Trevor Darrell / Kristen Grauman
Adriana Kovashka (University of Pittsburgh)
Sung Ju Hwang (Ulsan National Institute of Science and Technology)
Jaechul Kim (Amazon Research)
Yong Jae Lee (UC Davis)
Sudheendra Vijayanarasimhan (Google Research)

Awesome People in Computer Vision / Trevor Darrell
Kate Saenko (UMass Lowell)

Awesome People in Computer Vision
Irfan Essa (Georgia Institute of Technology)
Tony Jebara (Columbia University)
Baback Moghaddam (JPL)
Rosalind Picard (MIT Media Lab)
Stanley Sclaroff (Boston University)
Thad Starner (Georgia Institute of Technology)
Matthew Turk (University of California, Santa Barbara)
Vaibhav Vaish
David Koller
Billy Chen
Gaurav Garg
Natasha Gelfand (Nokia Research)
Augusto Roman (Flux Factory)
Li-Yi Wei (The University of Hong Kong)
Szymon Rusinkeiwicz (Princeton University)
Brian Curless (University of Washington)

Awesome People in Computer Vision / Brian Curless
Li Zhang (Google)
Yung-Yu Chuang (National Taiwan University)
Daniel Goldman (Adobe)

Awesome People in Computer Vision
Philippe Lacroute (Stanford University)

Awesome People in Computer Vision / Books
Computer Vision: Models, Learning, and Inference Simon J. D. Prince 2012
Computer Vision: Theory and Application Rick Szeliski 2010
Computer Vision: A Modern Approach (2nd edition) David Forsyth and Jean Ponce 2011
Multiple View Geometry in Computer Vision Richard Hartley and Andrew Zisserman 2004
Computer Vision Linda G. Shapiro 2001
Vision Science: Photons to Phenomenology Stephen E. Palmer 1999
Visual Object Recognition synthesis lecture Kristen Grauman and Bastian Leibe 2011
Computer Vision for Visual Effects Richard J. Radke, 2012
High dynamic range imaging: acquisition, display, and image-based lighting Reinhard, E., Heidrich, W., Debevec, P., Pattanaik, S., Ward, G., Myszkowski, K 2010
Learning OpenCV: Computer Vision with the OpenCV Library Gary Bradski and Adrian Kaehler
Practical Python and OpenCV Adrian Rosebrock
OpenCV Essentials Oscar Deniz Suarez, Mª del Milagro Fernandez Carrobles, Noelia Vallez Enano, Gloria Bueno Garcia, Ismael Serrano Gracia
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
Pattern Classification Peter E. Hart, David G. Stork, and Richard O. Duda 2000
Machine Learning Tom M. Mitchell 1997
Gaussian processes for machine learning Carl Edward Rasmussen and Christopher K. I. Williams 2005
Learning From Data Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin 2012
Neural Networks and Deep Learning Michael Nielsen 2014
Bayesian Reasoning and Machine Learning David Barber, Cambridge University Press, 2012
Linear Algebra and Its Applications Gilbert Strang 1995

Awesome People in Computer Vision / 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)
Computer Vision Steve Seitz (University of Washington)
Visual Recognition Kristen Grauman (UT Austin)
Language and Vision Tamara Berg (UNC Chapel Hill)
Convolutional Neural Networks for Visual Recognition Fei-Fei Li and Andrej Karpathy (Stanford University)
Computer Vision Rob Fergus (NYU)
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)
Image Manipulation and Computational Photography Alexei A. Efros (UC Berkeley)
Computational Photography Alexei A. Efros (CMU)
Computational Photography Derek Hoiem (UIUC)
Computational Photography James Hays (Brown University)
Digital & Computational Photography Fredo Durand (MIT)
Computational Camera and Photography Ramesh Raskar (MIT Media Lab)
Computational Photography Irfan Essa (Georgia Tech)
Courses in Graphics Stanford University
Computational Photography Rob Fergus (NYU)
Introduction to Visual Computing Kyros Kutulakos (University of Toronto)
Computational Photography Kyros Kutulakos (University of Toronto)
Computer Vision for Visual Effects Rich Radke (Rensselaer Polytechnic Institute)
Introduction to Image Processing Rich Radke (Rensselaer Polytechnic Institute)
Machine Learning Andrew Ng (Stanford University)
Learning from Data Yaser S. Abu-Mostafa (Caltech)
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)
Practical Machine Learning Michael Jordan (UC Berkeley)
Course on Information Theory, Pattern Recognition, and Neural Networks David MacKay (University of Cambridge)
Methods for Applied Statistics: Unsupervised Learning Lester Mackey (Stanford)
Machine Learning Andrew Zisserman (University of Oxford)
Convex Optimization I Stephen Boyd (Stanford University)
Convex Optimization II Stephen Boyd (Stanford University)
Convex Optimization Stephen Boyd (Stanford University)
Optimization at MIT (MIT)
Convex Optimization Ryan Tibshirani (CMU)

Awesome People in Computer Vision / Papers
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)
Visionbib Survey Paper List
Foundations and Trends® in Computer Graphics and Vision
Computer Vision: A Reference Guide

Awesome People in Computer Vision / 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
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 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 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
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
Object Recognition Larry Zitnick (Microsoft Research)
Generative Models for Visual Objects and Object Recognition via Bayesian Inference Fei-Fei Li (Stanford University)
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
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)
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) ( )
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

Awesome People in Computer Vision / Tutorials and talks / Machine Learning Summer School
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)

Awesome People in Computer Vision / Software
Computer Vision Resources Jia-Bin Huang (UIUC)
Computer Vision Algorithm Implementations CVPapers
Source Code Collection for Reproducible Research Xin Li (West Virginia University)
CMU Computer Vision Page
Open CV
mexopencv
SimpleCV
Open source Python module for computer vision 1,921 almost 4 years ago
ccv: A Modern Computer Vision Library 7,089 6 days ago
VLFeat
Matlab Computer Vision System Toolbox
Piotr's Computer Vision Matlab Toolbox
PCL: Point Cloud Library
ImageUtilities
MATLAB Functions for Multiple View Geometry
Peter Kovesi's Matlab Functions for Computer Vision and Image Analysis
OpenGV geometric computer vision algorithms
MinimalSolvers Minimal problems solver
Multi-View Environment
Visual SFM
Bundler SFM
openMVG: open Multiple View Geometry Multiple View Geometry; Structure from Motion library & softwares
Patch-based Multi-view Stereo V2
Clustering Views for Multi-view Stereo
Floating Scale Surface Reconstruction
Large-Scale Texturing of 3D Reconstructions
VLFeat
SIFT
SIFT++
BRISK
SURF
FREAK
AKAZE
Local Binary Patterns 97 almost 7 years ago
HDR_Toolbox 373 3 months ago
Middlebury Stereo Vision
The KITTI Vision Benchmark Suite
LIBELAS: Library for Efficient Large-scale Stereo Matching
Ground Truth Stixel Dataset
Middlebury Optical Flow Evaluation
MPI-Sintel Optical Flow Dataset and Evaluation
The KITTI Vision Benchmark Suite
HCI Challenge
Coarse2Fine Optical Flow Ce Liu (MIT)
Secrets of Optical Flow Estimation and Their Principles
C++/MatLab Optical Flow by C. Liu (based on Brox et al. and Bruhn et al.)
Parallel Robust Optical Flow by Sánchez Pérez et al.
Multi-frame image super-resolution
Markov Random Fields for Super-Resolution
Sparse regression and natural image prior
Single-Image Super Resolution via a Statistical Model
Sparse Coding for Super-Resolution
Patch-wise Sparse Recovery
Neighbor embedding
Deformable Patches
SRCNN
A+: Adjusted Anchored Neighborhood Regression
Transformed Self-Exemplars
Spatially variant non-blind deconvolution
Handling Outliers in Non-blind Image Deconvolution
Hyper-Laplacian Priors
From Learning Models of Natural Image Patches to Whole Image Restoration
Deep Convolutional Neural Network for Image Deconvolution
Neural Deconvolution
Removing Camera Shake From A Single Photograph
High-quality motion deblurring from a single image
Two-Phase Kernel Estimation for Robust Motion Deblurring
Blur kernel estimation using the radon transform
Fast motion deblurring
Blind Deconvolution Using a Normalized Sparsity Measure
Blur-kernel estimation from spectral irregularities
Efficient marginal likelihood optimization in blind deconvolution
Unnatural L0 Sparse Representation for Natural Image Deblurring
Edge-based Blur Kernel Estimation Using Patch Priors
Blind Deblurring Using Internal Patch Recurrence
Non-uniform Deblurring for Shaken Images
Single Image Deblurring Using Motion Density Functions
Image Deblurring using Inertial Measurement Sensors
Fast Removal of Non-uniform Camera Shake
GIMP Resynthesizer
Priority BP
ImageMelding
PlanarStructureCompletion
RetargetMe
Alpha Matting Evaluation
Closed-form image matting
Spectral Matting
Learning-based Matting
Improving Image Matting using Comprehensive Sampling Sets
The Steerable Pyramid
CurveLab
Fast Bilateral Filter
O(1) Bilateral Filter
Recursive Bilateral Filtering
Rolling Guidance Filter
Relative Total Variation
L0 Gradient Optimization
Domain Transform
Adaptive Manifold
Guided image filtering
Recovering Intrinsic Images with a global Sparsity Prior on Reflectance
Intrinsic Images by Clustering
Mean Shift Segmentation
Graph-based Segmentation
Normalized Cut
Grab Cut
Contour Detection and Image Segmentation
Structured Edge Detection
Pointwise Mutual Information
SLIC Super-pixel
QuickShift
TurboPixels
Entropy Rate Superpixel
Contour Relaxed Superpixels
SEEDS
SEEDS Revised 52 almost 6 years ago
Multiscale Combinatorial Grouping
Fast Edge Detection Using Structured Forests 827 almost 5 years ago
Random Walker
Geodesic Segmentation
Lazy Snapping
Power Watershed
Geodesic Graph Cut
Segmentation by Transduction
Video Segmentation with Superpixels
Efficient hierarchical graph-based video segmentation
Object segmentation in video
Streaming hierarchical video segmentation
Camera Calibration Toolbox for Matlab
Camera calibration With OpenCV
Multiple Camera Calibration Toolbox
openSLAM
Kitti Odometry: benchmark for outdoor visual odometry (codes may be available)
LIBVISO2: C++ Library for Visual Odometry 2
PTAM: Parallel tracking and mapping
KFusion: Implementation of KinectFusion 194 over 9 years ago
kinfu_remake: Lightweight, reworked and optimized version of Kinfu. 342 over 5 years ago
LVR-KinFu: kinfu_remake based Large Scale KinectFusion with online reconstruction
InfiniTAM: Implementation of multi-platform large-scale depth tracking and fusion
VoxelHashing: Large-scale KinectFusion 670 about 4 years ago
SLAMBench: Multiple-implementation of KinectFusion
SVO: Semi-direct visual odometry 2,104 about 5 years ago
DVO: dense visual odometry 642 about 8 years ago
FOVIS: RGB-D visual odometry
GTSAM: General smoothing and mapping library for Robotics and SFM -- Georgia Institute of Technology
G2O: General framework for graph optomization 3,092 4 days ago
FabMap: appearance-based loop closure system also available in
DBoW2: binary bag-of-words loop detection system
RatSLAM
LSD-SLAM 2,619 over 1 year ago
ORB-SLAM 1,524 about 2 years ago
Geometric Context Derek Hoiem (CMU)
Recovering Spatial Layout Varsha Hedau (UIUC)
Geometric Reasoning David C. Lee (CMU)
RGBD2Full3D 24 about 10 years ago Ruiqi Guo (UIUC)
INRIA Object Detection and Localization Toolkit
Discriminatively trained deformable part models
VOC-DPM 579 over 7 years ago
Histograms of Sparse Codes for Object Detection
R-CNN: Regions with Convolutional Neural Network Features 2,379 over 7 years ago
SPP-Net 364 over 8 years ago
BING: Objectness Estimation
Edge Boxes 827 almost 5 years ago
ReInspect
ANN: A Library for Approximate Nearest Neighbor Searching
FLANN - Fast Library for Approximate Nearest Neighbors
Fast k nearest neighbor search using GPU
PatchMatch
Generalized PatchMatch
Coherency Sensitive Hashing
PMBP: PatchMatch Belief Propagation 27 about 10 years ago
TreeCANN
Visual Tracker Benchmark
Visual Tracking Challenge
Kanade-Lucas-Tomasi Feature Tracker
Extended Lucas-Kanade Tracking
Online-boosting Tracking
Spatio-Temporal Context Learning
Locality Sensitive Histograms
Enhanced adaptive coupled-layer LGTracker++
TLD: Tracking - Learning - Detection
CMT: Clustering of Static-Adaptive Correspondences for Deformable Object Tracking
Kernelized Correlation Filters
Accurate Scale Estimation for Robust Visual Tracking
Multiple Experts using Entropy Minimization
TGPR
CF2: Hierarchical Convolutional Features for Visual Tracking
NeuralTalk 5,411 almost 4 years ago -
Ceres Solver Nonlinear least-square problem and unconstrained optimization solver
NLopt Nonlinear least-square problem and unconstrained optimization solver
OpenGM Factor graph based discrete optimization and inference solver
GTSAM Factor graph based lease-square optimization solver
Awesome Deep Vision 10,830 over 1 year ago
Awesome Machine Learning 66,046 10 days ago
Bob: a free signal processing and machine learning toolbox for researchers
LIBSVM -- A Library for Support Vector Machines

Awesome People in Computer Vision / Datasets
CV Datasets on the web CVPapers
Are we there yet? Which paper provides the best results on standard dataset X?
Computer Vision Dataset on the web
Yet Another Computer Vision Index To Datasets
ComputerVisionOnline Datasets
CVOnline Dataset
CV datasets
visionbib
Middlebury Stereo Vision
The KITTI Vision Benchmark Suite
LIBELAS: Library for Efficient Large-scale Stereo Matching
Ground Truth Stixel Dataset
Middlebury Optical Flow Evaluation
MPI-Sintel Optical Flow Dataset and Evaluation
The KITTI Vision Benchmark Suite
HCI Challenge
Single-Image Super-Resolution: A Benchmark
Ground-truth dataset and baseline evaluations for intrinsic image algorithms
Intrinsic Images in the Wild
Intrinsic Image Evaluation on Synthetic Complex Scenes
OpenSurface
Flickr Material Database
Materials in Context Dataset
Multi-View Stereo Reconstruction
Visual Tracker Benchmark
Visual Tracker Benchmark v1.1
VOT Challenge
Princeton Tracking Benchmark
VIRAT
CAM2
ChangeDetection.net
The PASCAL Visual Object Classes
ImageNet Large Scale Visual Recognition Challenge
SUN Database
Place Dataset
The PASCAL Visual Object Classes
ImageNet Object Detection Challenge
Microsoft COCO
Stanford background dataset
CamVid
Barcelona Dataset
SIFT Flow Dataset
3D Object Dataset
EPFL Car Dataset
KTTI Dection Dataset
SUN 3D Dataset
PASCAL 3D+
NYU Car Dataset
Fine-grained Classification Challenge
Caltech-UCSD Birds 200
Caltech Pedestrian Detection Benchmark
ETHZ Pedestrian Detection
HOLLYWOOD2 Dataset
UCF Sports Action Data Set
Sun dataset
Levin dataset
Flickr 8K
Flickr 30K
Microsoft COCO

NYU depth v2 - Indoor Segmentation and Support Inference from RGBD Images / 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)
Write Good Papers Frédo Durand (MIT)
Notes on writing Frédo Durand (MIT)
How to Write a Bad Article Frédo Durand (MIT)
How to write a good CVPR submission William T. Freeman (MIT)
How to write a great research paper Simon Peyton Jones (Microsoft Research)
How to write a SIGGRAPH paper SIGGRAPH ASIA 2011 Course
Writing Research Papers Aaron Hertzmann (Adobe Research)
How to Write a Paper for SIGGRAPH Jim Blinn
How to Get Your SIGGRAPH Paper Rejected Jim Kajiya (Microsoft Research)
How to write a SIGGRAPH paper Li-Yi Wei (The University of Hong Kong)
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)
Giving a Research Talk Frédo Durand (MIT)
How to give a good talk David Fleet (University of Toronto) and Aaron Hertzmann (Adobe Research)
Designing conference posters Colin Purrington
How to do research William T. Freeman (MIT)
You and Your Research Richard Hamming
Warning Signs of Bogus Progress in Research in an Age of Rich Computation and Information Yi Ma (UIUC)
Seven Warning Signs of Bogus Science Robert L. Park
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)
Recent Advances in Computer Vision Ming-Hsuan Yang (UC Merced)
How to Come Up with Research Ideas in Computer Vision? Jia-Bin Huang (UIUC)
How to Read Academic Papers Jia-Bin Huang (UIUC)
Time Management Randy Pausch (CMU)

NYU depth v2 - Indoor Segmentation and Support Inference from RGBD Images / Blogs
Learn OpenCV Satya Mallick
Tombone's Computer Vision Blog Tomasz Malisiewicz
Computer vision for dummies Vincent Spruyt
Andrej Karpathy blog Andrej Karpathy
AI Shack Utkarsh Sinha
Computer Vision Talks Eugene Khvedchenya
The Computer Vision Industry David Lowe
German Computer Vision Research Groups & Companies
awesome-deep-learning 24,271 7 months ago
awesome-maching-learning 66,046 10 days ago
Cat Paper Collection

NYU depth v2 - Indoor Segmentation and Support Inference from RGBD Images / Songs
The Fundamental Matrix Song
The RANSAC Song
Machine Learning A Cappella - Overfitting Thriller

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