The Ultimate List of 300+ Computer Vision Resources

A curated collection of 300+ awesome computer vision resources including books, courses, papers, tutorials, software and more.

Due to the size of this list, it can be hard to keep up with broken links, so if you come across any, please let me know in the comments section.

Also, if you know of any more awesome computer vision resources than what is on this list, please let me know in the comments section.

Table of Contents

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

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

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

Semantic Segmentation

• List of Semantic Segmentation algorithms

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.

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

Intrinsic Images

• Recovering Intrinsic Images with a global Sparsity Prior on Reflectance
• Intrinsic Images by Clustering

Contour Detection and Image Segmentation

• 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
• Multiscale Combinatorial Grouping
• Fast Edge Detection Using Structured Forests

Interactive Image Segmentation

• Random Walker
• Geodesic Segmentation
• Lazy Snapping
• Power Watershed
• Geodesic Graph Cut
• Segmentation by Transduction

Video Segmentation

• Video Segmentation with Superpixels
• Efficient hierarchical graph-based video segmentation
• Object segmentation in video
• Streaming hierarchical video segmentation

Camera calibration

• Camera Calibration Toolbox for Matlab
• Camera calibration With OpenCV
• Multiple Camera Calibration Toolbox

Simultaneous localization and mapping

SLAM community:

• openSLAM
• Kitti Odometry: benchmark for outdoor visual odometry (codes may be available)

Tracking/Odometry:

• LIBVISO2: C++ Library for Visual Odometry 2
• PTAM: Parallel tracking and mapping
• KFusion: Implementation of KinectFusion
• kinfu_remake: Lightweight, reworked and optimized version of Kinfu.
• 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
• SLAMBench: Multiple-implementation of KinectFusion
• SVO: Semi-direct visual odometry
• DVO: dense visual odometry
• FOVIS: RGB-D visual odometry

Graph Optimization:

• GTSAM: General smoothing and mapping library for Robotics and SFM — Georgia Institute of Technology
• G2O: General framework for graph optomization

Loop Closure:

• FabMap: appearance-based loop closure system – also available inOpenCV2.4.11
• DBoW2: binary bag-of-words loop detection system

Localization & Mapping:

• RatSLAM
• LSD-SLAM
• ORB-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 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
• ReInspect

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
• 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
• Modular Tracking Framework

Image Captioning

• NeuralTalk – NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.

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

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

Visual Tracking

• Visual Tracker Benchmark
• Visual Tracker Benchmark v1.1
• VOT Challenge
• Princeton Tracking Benchmark
• Tracking Manipulation Tasks (TMT)

Visual Surveillance

• VIRAT
• CAM2

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

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)

Links

• The Computer Vision Industry – David Lowe
• German Computer Vision Research Groups & Companies
• Cat Paper Collection

原文地址：https://hackerlists.com/computer-vision-resources/