Table of Contents
BooksCoursesPapersSoftwareDatasetsTutorials and TalksResources for studentsBlogsLinksSongs
Books
Computer Vision
Computer Vision: Models, Learning, and Inference - Simon J. D. Prince 2012Computer Vision: Theory and Application - Rick Szeliski 2010Computer Vision: A Modern Approach (2nd edition) - David Forsyth and Jean Ponce 2011Multiple View Geometry in Computer Vision - Richard Hartley and Andrew Zisserman 2004Computer Vision - Linda G. Shapiro 2001Vision Science: Photons to Phenomenology - Stephen E. Palmer 1999Visual Object Recognition synthesis lecture - Kristen Grauman and Bastian Leibe 2011
OpenCV Programming
Learning OpenCV: Computer Vision with the OpenCV Library - Gary Bradski and Adrian KaehlerPractical Python and OpenCV - Adrian RosebrockOpenCV Essentials - Oscar Deniz Suarez, Mª del Milagro Fernandez Carrobles, Noelia Vallez Enano, Gloria Bueno Garcia, Ismael Serrano Gracia
Machine Learning
Pattern Recognition and Machine Learning - Christopher M. Bishop 2007Neural Networks for Pattern Recognition - Christopher M. Bishop 1995Probabilistic Graphical Models: Principles and Techniques - Daphne Koller and Nir Friedman 2009Pattern Classification - Peter E. Hart, David G. Stork, and Richard O. Duda 2000Machine Learning - Tom M. Mitchell 1997Gaussian processes for machine learning - Carl Edward Rasmussen and Christopher K. I. Williams 2005Learning From Data- Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin 2012Neural Networks and Deep Learning - Michael Nielsen 2014Bayesian Reasoning and Machine Learning - David Barber, Cambridge University Press, 2012
Fundamentals
Linear Algebra and Its Applications - Gilbert Strang 1995
Courses
Computer Vision
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)
Computational Photography
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 UniversityComputational Photography - Rob Fergus (NYU)Introduction to Visual Computing - Kyros Kutulakos (University of Toronto)Computational Photography - Kyros Kutulakos (University of Toronto)
Machine Learning and Statistical Learning
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)
Optimization
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)
Papers
Conference papers on the web
CVPapers - Computer vision papers on the webSIGGRAPH Paper on the web - Graphics papers on the webNIPS Proceedings - NIPS papers on the webComputer Vision Foundation open accessAnnotated Computer Vision Bibliography - Keith Price (USC)Calendar of Computer Image Analysis, Computer Vision Conferences - (USC)
Survey Papers
Visionbib Survey Paper ListFoundations and Trends® in Computer Graphics and VisionComputer Vision: A Reference Guide
Tutorials and talks
Computer Vision
Computer Vision Talks - Lectures, keynotes, panel discussions on computer visionThe Three R's of Computer Vision - Jitendra Malik (UC Berkeley) 2013Applications to Machine Vision - Andrew Blake (Microsoft Research) 2008The Future of Image Search - Jitendra Malik (UC Berkeley) 2008Should I do a PhD in Computer Vision? - Fatih Porikli (Australian National University)Graduate Summer School 2013: Computer Vision - IPAM, 2013
Recent Conference Talks
CVPR 2015 - Jun 2015ECCV 2014 - Sep 2014CVPR 2014 - Jun 2014ICCV 2013 - Dec 2013CVPR 2013 - Jun 2013ECCV 2012 - Oct 2012CVPR 2012 - Jun 2012
3D Computer Vision
3D Computer Vision: Past, Present, and Future - Steve Seitz (University of Washington) 2011Reconstructing the World from Photos on the Internet - Steve Seitz (University of Washington) 2013
Internet Vision
The Distributed Camera - Noah Snavely (Cornell University) 2011Planet-Scale Visual Understanding - Noah Snavely (Cornell University) 2014A Trillion Photos - Steve Seitz (University of Washington) 2013
Computational Photography
Reflections on Image-Based Modeling and Rendering - Richard Szeliski (Microsoft Research) 2013Photographing Events over Time - William T. Freeman (MIT) 2011Old and New algorithm for Blind Deconvolution - Yair Weiss (The Hebrew University of Jerusalem) 2011A Tour of Modern "Image Processing" - Peyman Milanfar (UC Santa Cruz/Google) 2010Topics in image and video processing Andrew Blake (Microsoft Research) 2007Computational Photography - William T. Freeman (MIT) 2012Revealing the Invisible - Frédo Durand (MIT) 2012
Learning and Vision
Where machine vision needs help from machine learning - William T. Freeman (MIT) 2011Learning in Computer Vision - Simon Lucey (CMU) 2008Learning and Inference in Low-Level Vision - Yair Weiss (The Hebrew University of Jerusalem) 2009
Object Recognition
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) 2012Graphical Models - Zoubin Ghahramani (University of Cambridge) 2009Machine Learning, Probability and Graphical Models - Sam Roweis (NYU) 2006Graphical Models and Applications - Yair Weiss (The Hebrew University of Jerusalem) 2009
Machine Learning
A Gentle Tutorial of the EM Algorithm - Jeff A. Bilmes (UC Berkeley) 1998Introduction To Bayesian Inference - Christopher Bishop (Microsoft Research) 2009Support Vector Machines - Chih-Jen Lin (National Taiwan University) 2006Bayesian or Frequentist, Which Are You? - Michael I. Jordan (UC Berkeley)
Optimization
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) 2014Deep 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, 2012Workshop on Big Data and Statistical Machine LearningMachine 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)
Software
External Resource Links
Computer Vision Resources - Jia-Bin Huang (UIUC)Computer Vision Algorithm Implementations - CVPapersSource Code Collection for Reproducible Research - Xin Li (West Virginia University)CMU Computer Vision Page
General Purpose Computer Vision Library
Open CVSimpleCVOpen source Python module for computer visionccv: A Modern Computer Vision LibraryVLFeatMatlab Computer Vision System ToolboxPiotr's Computer Vision Matlab ToolboxPCL: Point Cloud LibraryImageUtilities
Multiple-view Computer Vision
MATLAB Functions for Multiple View GeometryPeter Kovesi's Matlab Functions for Computer Vision and Image AnalysisOpenGV - geometric computer vision algorithmsMinimalSolvers - Minimal problems solverMulti-View EnvironmentVisual SFMBundler SFMopenMVG: open Multiple View Geometry - Multiple View Geometry; Structure from Motion library & softwaresPatch-based Multi-view Stereo V2Clustering Views for Multi-view StereoFloating Scale Surface ReconstructionLarge-Scale Texturing of 3D Reconstructions
Feature Detection and Extraction
VLFeatSIFT
David G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, 60, 2 (2004), pp. 91-110. SIFT++BRISK
Stefan Leutenegger, Margarita Chli and Roland Siegwart, "BRISK: Binary Robust Invariant Scalable Keypoints", ICCV 2011 SURF
Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool, "SURF: Speeded Up Robust Features", Computer Vision and Image Understanding (CVIU), Vol. 110, No. 3, pp. 346--359, 2008 FREAK
A. Alahi, R. Ortiz, and P. Vandergheynst, "FREAK: Fast Retina Keypoint", CVPR 2012 AKAZE
Pablo F. Alcantarilla, Adrien Bartoli and Andrew J. Davison, "KAZE Features", ECCV 2012 Local Binary Patterns
Low-level Vision
Stereo Vision
Middlebury Stereo VisionThe KITTI Vision Benchmark SuiteLIBELAS: Library for Efficient Large-scale Stereo MatchingGround Truth Stixel Dataset
Optical Flow
Middlebury Optical Flow EvaluationMPI-Sintel Optical Flow Dataset and EvaluationThe KITTI Vision Benchmark SuiteHCI ChallengeCoarse2Fine Optical Flow - Ce Liu (MIT)Secrets of Optical Flow Estimation and Their PrinciplesC++/MatLab Optical Flow by C. Liu (based on Brox et al. and Bruhn et al.)Parallel Robust Optical Flow by Sánchez Pérez et al.
Image Denoising
BM3D, KSVD,
Super-resolution
Multi-frame image super-resolution
Pickup, L. C. Machine Learning in Multi-frame Image Super-resolution, PhD thesis 2008 Markov Random Fields for Super-Resolution
W. T Freeman and C. Liu. Markov Random Fields for Super-resolution and Texture Synthesis. In A. Blake, P. Kohli, and C. Rother, eds., Advances in Markov Random Fields for Vision and Image Processing, Chapter 10. MIT Press, 2011 Sparse regression and natural image prior
K. I. Kim and Y. Kwon, "Single-image super-resolution using sparse regression and natural image prior", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 6, pp. 1127-1133, 2010. Single-Image Super Resolution via a Statistical Model
T. Peleg and M. Elad, A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution, IEEE Transactions on Image Processing, Vol. 23, No. 6, Pages 2569-2582, June 2014 Sparse Coding for Super-Resolution
R. Zeyde, M. Elad, and M. Protter On Single Image Scale-Up using Sparse-Representations, Curves & Surfaces, Avignon-France, June 24-30, 2010 (appears also in Lecture-Notes-on-Computer-Science - LNCS). Patch-wise Sparse Recovery
Jianchao Yang, John Wright, Thomas Huang, and Yi Ma. Image super-resolution via sparse representation. IEEE Transactions on Image Processing (TIP), vol. 19, issue 11, 2010. Neighbor embedding
H. Chang, D.Y. Yeung, Y. Xiong. Super-resolution through neighbor embedding. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol.1, pp.275-282, Washington, DC, USA, 27 June - 2 July 2004. Deformable Patches
Yu Zhu, Yanning Zhang and Alan Yuille, Single Image Super-resolution using Deformable Patches, CVPR 2014 SRCNN
Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Learning a Deep Convolutional Network for Image Super-Resolution, in ECCV 2014 A+: Adjusted Anchored Neighborhood Regression
R. Timofte, V. De Smet, and L. Van Gool. A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution, ACCV 2014 Transformed Self-Exemplars
Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja, Single Image Super-Resolution using Transformed Self-Exemplars, IEEE Conference on Computer Vision and Pattern Recognition, 2015
Image Deblurring
Non-blind deconvolution
Spatially variant non-blind deconvolutionHandling Outliers in Non-blind Image DeconvolutionHyper-Laplacian PriorsFrom Learning Models of Natural Image Patches to Whole Image RestorationDeep Convolutional Neural Network for Image DeconvolutionNeural Deconvolution
Blind deconvolution
Removing Camera Shake From A Single PhotographHigh-quality motion deblurring from a single imageTwo-Phase Kernel Estimation for Robust Motion DeblurringBlur kernel estimation using the radon transformFast motion deblurringBlind Deconvolution Using a Normalized Sparsity MeasureBlur-kernel estimation from spectral irregularitiesEfficient marginal likelihood optimization in blind deconvolutionUnnatural L0 Sparse Representation for Natural Image DeblurringEdge-based Blur Kernel Estimation Using Patch PriorsBlind Deblurring Using Internal Patch Recurrence
Non-uniform Deblurring
Non-uniform Deblurring for Shaken ImagesSingle Image Deblurring Using Motion Density FunctionsImage Deblurring using Inertial Measurement SensorsFast Removal of Non-uniform Camera Shake
Image Completion
GIMP ResynthesizerPriority BPImageMeldingPlanarStructureCompletion
Image Retargeting
RetargetMe
Alpha Matting
Alpha Matting EvaluationClosed-form image mattingSpectral MattingLearning-based MattingImproving Image Matting using Comprehensive Sampling Sets
Image Pyramid
The Steerable PyramidCurveLab
Edge-preserving image processing
Fast Bilateral FilterO(1) Bilateral FilterRecursive Bilateral FilteringRolling Guidance FilterRelative Total VariationL0 Gradient OptimizationDomain TransformAdaptive ManifoldGuided image filtering
Intrinsic Images
Recovering Intrinsic Images with a global Sparsity Prior on ReflectanceIntrinsic Images by Clustering
Contour Detection and Image Segmentation
Mean Shift SegmentationGraph-based SegmentationNormalized CutGrab CutContour Detection and Image SegmentationStructured Edge DetectionPointwise Mutual InformationSLIC Super-pixelQuickShiftTurboPixelsEntropy Rate SuperpixelContour Relaxed SuperpixelsSEEDSSEEDS RevisedMultiscale Combinatorial GroupingFast Edge Detection Using Structured Forests
Interactive Image Segmentation
Random WalkerGeodesic SegmentationLazy SnappingPower WatershedGeodesic Graph CutSegmentation by Transduction
Video Segmentation
Video Segmentation with SuperpixelsEfficient hierarchical graph-based video segmentationObject segmentation in videoStreaming hierarchical video segmentation
Camera calibration
Camera Calibration Toolbox for MatlabCamera calibration With OpenCVMultiple Camera Calibration Toolbox
Simultaneous localization and mapping
SLAM community:
openSLAMKitti Odometry: benchmark for outdoor visual odometry (codes may be available)
Tracking/Odometry:
LIBVISO2: C++ Library for Visual Odometry 2PTAM: Parallel tracking and mappingKFusion: Implementation of KinectFusionInfiniTAM: Implementation of multi-platform large-scale depth tracking and fusionVoxelHashing: Large-scale KinectFusionSLAMBench: Multiple-implementation of KinectFusionSVO: Semi-direct visual odometryDVO: dense visual odometryFOVIS: RGB-D visual odometry
Graph Optimization:
GTSAM: General smoothing and mapping library for Robotics and SFM -- Georgia Institute of TechnologyG2O: General framework for graph optomization
Loop Closure:
FabMap: appearance-based loop closure system - also available in OpenCV2.4.11DBoW2: binary bag-of-words loop detection system
Localization & Mapping:
RatSLAMLSD-SLAMORB-SLAM
Single-view Spatial Understanding
Geometric Context - Derek Hoiem (CMU)Recovering Spatial Layout - Varsha Hedau (UIUC)Geometric Reasoning - David C. Lee (CMU)RGBD2Full3D - Ruiqi Guo (UIUC)
Object Detection
INRIA Object Detection and Localization ToolkitDiscriminatively trained deformable part modelsVOC-DPMHistograms of Sparse Codes for Object DetectionR-CNN: Regions with Convolutional Neural Network FeaturesSPP-NetBING: Objectness EstimationEdge Boxes
Nearest Neighbor Search
General purpose nearest neighbor search
ANN: A Library for Approximate Nearest Neighbor SearchingFLANN - Fast Library for Approximate Nearest NeighborsFast k nearest neighbor search using GPU
Nearest Neighbor Field Estimation
PatchMatchGeneralized PatchMatchCoherency Sensitive HashingPMBP: PatchMatch Belief PropagationTreeCANN
Visual Tracking
Visual Tracker BenchmarkVisual Tracking ChallengeKanade-Lucas-Tomasi Feature TrackerOnline-boosting TrackingMultiple Experts using Entropy MinimizationKernelized Correlation FiltersTGPRExtended Lucas-Kanade TrackingSpatio-Temporal Context LearningLocality Sensitive HistogramsStructure Preserving Object TrackerAdaptive Color Attributes
Saliency Detection
Attributes
Action Reconition
Egocentric cameras
Human-in-the-loop systems
Image Captioning
NeuralTalk -
Optimization
Ceres Solver - Nonlinear least-square problem and unconstrained optimization solverNLopt- Nonlinear least-square problem and unconstrained optimization solverOpenGM - Factor graph based discrete optimization and inference solverGTSAM - Factor graph based lease-square optimization solver
Deep Learning
Awesome Deep Vision
Machine Learning
Awesome Machine LearningBob: a free signal processing and machine learning toolbox for researchers
Datasets
External Dataset Link Collection
CV Datasets on the web - CVPapersAre we there yet? - Which paper provides the best results on standard dataset X?Computer Vision Dataset on the webYet Another Computer Vision Index To DatasetsComputerVisionOnline DatasetsCVOnline DatasetCV datasetsvisionbib
Low-level Vision
Stereo Vision
Middlebury Stereo VisionThe KITTI Vision Benchmark SuiteLIBELAS: Library for Efficient Large-scale Stereo MatchingGround Truth Stixel Dataset
Optical Flow
Middlebury Optical Flow EvaluationMPI-Sintel Optical Flow Dataset and EvaluationThe KITTI Vision Benchmark SuiteHCI Challenge
Image Super-resolutions
Single-Image Super-Resolution: A Benchmark
Intrinsic Images
Ground-truth dataset and baseline evaluations for intrinsic image algorithmsIntrinsic Images in the WildIntrinsic Image Evaluation on Synthetic Complex Scenes
Material Recognition
OpenSurfaceFlickr Material DatabaseMaterials in Context Dataset
Multi-view Reconsturction
Multi-View Stereo Reconstruction
Saliency Detection
Visual Tracking
Visual Tracker BenchmarkVisual Tracker Benchmark v1.1VOT ChallengePrinceton Tracking Benchmark
Visual Surveillance
VIRATCAM2
Saliency Detection
Change detection
ChangeDetection.net
Visual Recognition
Image Classification
The PASCAL Visual Object ClassesImageNet Large Scale Visual Recognition Challenge
Scene Recognition
SUN DatabasePlace Dataset
Object Detection
The PASCAL Visual Object ClassesImageNet Object Detection ChallengeMicrosoft COCO
Semantic labeling
Stanford background datasetCamVidBarcelona DatasetSIFT Flow Dataset
Multi-view Object Detection
3D Object DatasetEPFL Car DatasetKTTI Dection DatasetSUN 3D DatasetPASCAL 3D+NYU Car Dataset
Fine-grained Visual Recognition
Fine-grained Classification ChallengeCaltech-UCSD Birds 200
Pedestrian Detection
Caltech Pedestrian Detection BenchmarkETHZ Pedestrian Detection
Action Recognition
Image-based
Video-based
HOLLYWOOD2 DatasetUCF Sports Action Data Set
Image Deblurring
Sun datasetLevin dataset
Image Captioning
Flickr 8KFlickr 30KMicrosoft COCO
Scene Understanding
# SUN RGB-D - A RGB-D Scene Understanding Benchmark Suite # NYU depth v2 - Indoor Segmentation and Support Inference from RGBD Images
Resources for students
Resource link collection
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)
Writing
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 CourseWriting Research Papers - Aaron Hertzmann (Adobe Research)How to Write a Paper for SIGGRAPH - Jim BlinnHow 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)
Presentation
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
Research
How to do research - William T. Freeman (MIT)You and Your Research - Richard HammingWarning 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. ParkFive 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
Time Management - Randy Pausch (CMU)
Blogs
Learn OpenCV - Satya MallickTombone's Computer Vision Blog - Tomasz MalisiewiczComputer vision for dummies - Vincent SpruytAndrej Karpathy blog - Andrej Karpathy
Links
The Computer Vision Industry - David LoweGerman Computer Vision Research Groups & Companiesawesome-deep-learningawesome-maching-learningCat Paper Collection
Songs
The Fundamental Matrix SongThe RANSAC SongMachine Learning A Cappella - Overfitting Thriller
