github上计算机视觉学习资源

Awesome Computer Vision: 

A curated list of awesome computer vision resources, inspired by awesome-php.

For a list people in computer vision listed with their academic genealogy, please visit here

Contributing

Please feel free to send me pull requests or email ([email protected]) to add links.

Table of Contents

  • Books
  • Courses
  • Papers
  • Software
  • Datasets
  • Tutorials and Talks
  • Resources for students
  • Blogs
  • Links
  • Songs

Books

Computer Vision

  • 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

OpenCV Programming

  • 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

Machine Learning

  • 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

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 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 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 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

Tutorials and talks

Computer Vision

  • 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

Recent Conference Talks

  • 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

  • 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

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) 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

Machine Learning

  • 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

  • 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)

Software

External Resource Links

  • 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

General Purpose Computer Vision Library

  • Open CV
  • mexopencv
  • SimpleCV
  • Open source Python module for computer vision
  • ccv: A Modern Computer Vision Library
  • VLFeat
  • Matlab Computer Vision System Toolbox
  • Piotr's Computer Vision Matlab Toolbox
  • PCL: Point Cloud Library
  • ImageUtilities

Multiple-view Computer Vision

  • 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

Feature Detection and Extraction

  • VLFeat
  • SIFT
    • 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

High Dynamic Range Imaging

  • HDR_Toolbox

Low-level Vision

Stereo Vision
  • Middlebury Stereo Vision
  • The KITTI Vision Benchmark Suite
  • LIBELAS: Library for Efficient Large-scale Stereo Matching
  • Ground Truth Stixel Dataset
Optical Flow
  • 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.
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 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

Blind 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

  • 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
Image Completion
  • GIMP Resynthesizer
  • Priority BP
  • ImageMelding
  • PlanarStructureCompletion
Image Retargeting
  • RetargetMe
Alpha Matting
  • Alpha Matting Evaluation
  • Closed-form image matting
  • Spectral Matting
  • Learning-based Matting
  • Improving Image Matting using Comprehensive Sampling Sets
Image Pyramid
  • The Steerable Pyramid
  • CurveLab
Edge-preserving image processing
  • 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

Object Detection

  • INRIA Object Detection and Localization Toolkit
  • Discriminatively trained deformable part models
  • VOC-DPM
  • Histograms of Sparse Codes for Object Detection
  • R-CNN: Regions with Convolutional Neural Network Features
  • SPP-Net
  • BING: Objectness Estimation
  • Edge Boxes

Nearest Neighbor Search

General purpose nearest neighbor search
  • ANN: A Library for Approximate Nearest Neighbor Searching
  • FLANN - Fast Library for Approximate Nearest Neighbors
  • Fast k nearest neighbor search using GPU
Nearest Neighbor Field Estimation
  • PatchMatch
  • Generalized PatchMatch
  • Coherency Sensitive Hashing
  • PMBP: PatchMatch Belief Propagation
  • TreeCANN

Visual Tracking

  • Visual Tracker Benchmark
  • Visual Tracking Challenge
  • Kanade-Lucas-Tomasi Feature Tracker
  • Online-boosting Tracking
  • Multiple Experts using Entropy Minimization
  • Kernelized Correlation Filters
  • TGPR
  • Extended Lucas-Kanade Tracking
  • Spatio-Temporal Context Learning
  • Locality Sensitive Histograms
  • Structure Preserving Object Tracker
  • Adaptive 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 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

Deep Learning

  • Awesome Deep Vision

Machine Learning

  • Awesome Machine Learning
  • Bob: a free signal processing and machine learning toolbox for researchers
  • LIBSVM -- A Library for Support Vector Machines

Datasets

External Dataset Link Collection

  • 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

Low-level Vision

Stereo Vision
  • Middlebury Stereo Vision
  • The KITTI Vision Benchmark Suite
  • LIBELAS: Library for Efficient Large-scale Stereo Matching
  • Ground Truth Stixel Dataset
Optical Flow
  • Middlebury Optical Flow Evaluation
  • MPI-Sintel Optical Flow Dataset and Evaluation
  • The KITTI Vision Benchmark Suite
  • HCI Challenge
Image Super-resolutions
  • Single-Image Super-Resolution: A Benchmark

Intrinsic Images

  • Ground-truth dataset and baseline evaluations for intrinsic image algorithms
  • Intrinsic Images in the Wild
  • Intrinsic Image Evaluation on Synthetic Complex Scenes

Material Recognition

  • OpenSurface
  • Flickr Material Database
  • Materials in Context Dataset

Multi-view Reconsturction

  • Multi-View Stereo Reconstruction

Saliency Detection

Visual Tracking

  • Visual Tracker Benchmark
  • Visual Tracker Benchmark v1.1
  • VOT Challenge
  • Princeton Tracking Benchmark

Visual Surveillance

  • VIRAT
  • CAM2

Saliency Detection

Change detection

  • ChangeDetection.net

Visual Recognition

Image Classification
  • The PASCAL Visual Object Classes
  • ImageNet Large Scale Visual Recognition Challenge
Scene Recognition
  • SUN Database
  • Place Dataset
Object Detection
  • The PASCAL Visual Object Classes
  • ImageNet Object Detection Challenge
  • Microsoft COCO
Semantic labeling
  • Stanford background dataset
  • CamVid
  • Barcelona Dataset
  • SIFT Flow Dataset
Multi-view Object Detection
  • 3D Object Dataset
  • EPFL Car Dataset
  • KTTI Dection Dataset
  • SUN 3D Dataset
  • PASCAL 3D+
  • NYU Car Dataset
Fine-grained Visual Recognition
  • Fine-grained Classification Challenge
  • Caltech-UCSD Birds 200
Pedestrian Detection
  • Caltech Pedestrian Detection Benchmark
  • ETHZ Pedestrian Detection

Action Recognition

Image-based
Video-based
  • HOLLYWOOD2 Dataset
  • UCF Sports Action Data Set
Image Deblurring
  • Sun dataset
  • Levin dataset

Image Captioning

  • Flickr 8K
  • Flickr 30K
  • Microsoft 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 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)

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 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

  • Time Management - Randy Pausch (CMU)

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

Links

  • The Computer Vision Industry - David Lowe
  • German Computer Vision Research Groups & Companies
  • awesome-deep-learning
  • awesome-maching-learning
  • Cat Paper Collection

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