Visual Recognition / Self-supervised Learning

• PASS: An An ImageNet replacement for self-supervised pretraining without humans (⭐247)

Awesome Lists

• Awesome Document Understanding (⭐519)

Pre-trained Computer Vision Models

• List of Computer Vision models (⭐28) These models are trained on custom objects

Awesome Lists

• Awesome Learning with Label Noise (⭐2.1k)

• Awesome Deblurring (⭐1.6k)

• Awsome Deep Geometry Learning (⭐291)

• Awesome Image Distortion Correction (⭐132)

• Awesome Neuron Segmentation in EM Images (⭐30)

• Awsome Delineation (⭐18)

• Awesome ImageHarmonization (⭐14)

• Awsome GAN Training (⭐12)

Awesome Lists

• Awesome Domain Adaptation (⭐4k)

• Awesome Object Detection (⭐7k)

• Awesome 3D Machine Learning (⭐8.4k)

• Awesome Action Recognition (⭐3.4k)

• Awesome Scene Understanding (⭐485)

• Awesome Adversarial Machine Learning (⭐1.6k)

• Awesome Adversarial Deep Learning (⭐235)

• Awesome Face (⭐828)

• Awesome Face Recognition (⭐3.7k)

• Awesome Human Pose Estimation (⭐1.1k)

• Awesome medical imaging (⭐161)

• Awesome Images (⭐2.3k)

• Awesome Graphics (⭐930)

• Awesome Neural Radiance Fields (⭐3.5k)

• Awesome Implicit Neural Representations (⭐1.9k)

• Awesome Neural Rendering (⭐1.7k)

• Awesome Public Datasets (⭐52k)

• Awesome Dataset Tools (⭐649)

• Awesome Robotics Datasets (⭐222)

• Awesome Mobile Machine Learning

• Awesome Explainable AI (⭐749)

• Awesome Fairness in AI (⭐224)

• Awesome Machine Learning Interpretability (⭐2.7k)

• Awesome Production Machine Learning (⭐12k)

• Awesome Video Text Retrieval (⭐327)

• Awesome Image-to-Image Translation (⭐831)

• Awesome Image Inpainting (⭐1.2k)

• Awesome Deep HDR (⭐308)

• Awesome Video Generation (⭐62)

• Awesome GAN applications (⭐4.4k)

• Awesome Generative Modeling (⭐152)

• Awesome Image Classification (⭐2.3k)

• Awesome Deep Learning (⭐20k)

• Awesome Machine Learning in Biomedical(Healthcare) Imaging (⭐37)

• Awesome Deep Learning for Tracking and Detection (⭐2.1k)

• Awesome Deep Learning for Video Analysis (⭐598)

• Awesome Vision + Language (⭐927)

• Awesome Robotics (⭐3k)

• Awesome Visual Transformer (⭐2.7k)

• Awesome Embodied Vision (⭐211)

• Awesome Anomaly Detection (⭐2.2k)

• Awesome Makeup Transfer (⭐70)

Computer Vision

• Computer Vision, From 3D Reconstruction to Recognition - Silvio Savarese 2018

Computer Vision

• Image Processing and Analysis - Stan Birchfield 2018

Annotation tools

• Comma Coloring

• Annotorious

• LabelME

• gtmaker (⭐10)

Machine Learning and Statistical Learning

• Machine Learning for Computer Vision - Rudolph Triebel (TU Munich)

Blogs / Image Deblurring

• Computer Vision Basics with Python Keras and OpenCV (⭐416) - Jason Chin (University of Western Ontario)

External Dataset Link Collection / Nearest Neighbor Field Estimation

• VisualData

Low-level Vision / Video Object Segmentation

• DAVIS: Densely Annotated VIdeo Segmentation

• SegTrack v2

Computer Vision

• Computer Vision Pascal Fua (EPFL):

• Computer Vision 1 Carsten Rother (TU Dresden):

• Computer Vision 2 Carsten Rother (TU Dresden):

• Multiple View Geometry Daniel Cremers (TU Munich):

Machine Learning and Statistical Learning

• (Convolutional) Neural Networks for Visual Recognition - Fei-Fei Li, Andrej Karphaty, Justin Johnson (Stanford University)

Computer Vision

• Visual Recognition Spring 2016, Fall 2016 - Kristen Grauman (UT Austin)

• Computer Vision - Bastian Leibe (RWTH Aachen University)

• Computer Vision 2 - Bastian Leibe (RWTH Aachen University)

Computer Vision

• Advances in Computer Vision - Antonio Torralba and Bill Freeman (MIT)

Computational Photography

• Computational Photography - Irfan Essa (Georgia Tech)

Machine Learning and Statistical Learning

• Intro to Machine Learning - Sebastian Thrun (Stanford University)

• Machine Learning - Charles Isbell, Michael Littman (Georgia Tech)

Links / Image Deblurring

Links / Image Deblurring

• awesome-machine-learning (⭐56k)

Low-level Vision / Change Detection

• Labeled and Annotated Sequences for Integral Evaluation of SegmenTation Algorithms

• ChangeDetection.net

Multiple-view Computer Vision

• Large-Scale Texturing of 3D Reconstructions

• Awesome 3D reconstruction list (⭐3.5k)

Links / Image Deblurring

• Computer Vision News

Computer Vision

• Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics - Justin Solomon 2015

Visual Tracking / Nearest Neighbor Field Estimation

• Modular Tracking Framework

Semantic Segmentation

• List of Semantic Segmentation algorithms

Visual Tracking / Image Super-resolutions

• Tracking Manipulation Tasks (TMT)

Computational Photography

• 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)

Object Recognition

• Object Recognition - Larry Zitnick (Microsoft Research)

Machine Learning

• A Gentle Tutorial of the EM Algorithm - Jeff A. Bilmes (UC Berkeley) 1998

Multiple-view Computer Vision

• Peter Kovesi's Matlab Functions for Computer Vision and Image Analysis

• MinimalSolvers - Minimal problems solver

• Multi-View Environment

Contour Detection and Image Segmentation / Edge-preserving image processing

• SLIC Super-pixel

• Contour Relaxed Superpixels

Simultaneous localization and mapping / Loop Closure:

• FabMap: appearance-based loop closure system - also available in OpenCV2.4.11

Links / Image Deblurring

• awesome-deep-learning (⭐20k)

Visual Tracking / Nearest Neighbor Field Estimation

• Online-boosting Tracking

• Enhanced adaptive coupled-layer LGTracker++

• TLD: Tracking - Learning - Detection

• Kernelized Correlation Filters

• Accurate Scale Estimation for Robust Visual Tracking

• Multiple Experts using Entropy Minimization

• TGPR

Visual Tracking / Nearest Neighbor Field Estimation

• CF2: Hierarchical Convolutional Features for Visual Tracking

Simultaneous localization and mapping / Tracking/Odometry:

• LVR-KinFu: kinfu_remake based Large Scale KinectFusion with online reconstruction

Machine Learning and Statistical Learning

• Course on Information Theory, Pattern Recognition, and Neural Networks - David MacKay (University of Cambridge)

External Resource Links

• CMU Computer Vision Page

Low-level Vision / Image Completion

• ImageMelding

Low-level Vision / Image Retargeting

• RetargetMe

Contour Detection and Image Segmentation / Edge-preserving image processing

• SEEDS Revised (⭐50)

Simultaneous localization and mapping / Tracking/Odometry:

• LIBVISO2: C++ Library for Visual Odometry 2

• KFusion: Implementation of KinectFusion (⭐190)

Simultaneous localization and mapping / Localization & Mapping:

• LSD-SLAM (⭐2.4k)

Image Captioning / Nearest Neighbor Field Estimation

• NeuralTalk -

Blogs / Image Deblurring

• Computer Vision Talks - Eugene Khvedchenya

Visual Tracking / Nearest Neighbor Field Estimation

• CMT: Clustering of Static-Adaptive Correspondences for Deformable Object Tracking

Object Detection / Localization & Mapping:

• ReInspect

Computer Vision

• 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

Machine Learning

• Gaussian processes for machine learning - Carl Edward Rasmussen and Christopher K. I. Williams 2005

Recent Conference Talks

• ICML 2013 - Jul 2013

• ICML 2012 - Jun 2012

Computational Photography

• Overview of Computer Vision and Visual Effects - Rich Radke (Rensselaer Polytechnic Institute) 2014

General Purpose Computer Vision Library

• mexopencv

High Dynamic Range Imaging

• HDR_Toolbox (⭐309)

Machine Learning / Nearest Neighbor Field Estimation

• LIBSVM -- A Library for Support Vector Machines

Simultaneous localization and mapping / Tracking/Odometry:

• kinfu_remake: Lightweight, reworked and optimized version of Kinfu. (⭐324)

Blogs / Image Deblurring

• AI Shack - Utkarsh Sinha

Deep Learning / Nearest Neighbor Field Estimation

• Awesome Deep Vision (⭐10k)

Material Recognition / Image Super-resolutions

• Materials in Context Dataset

Visual Recognition / Pedestrian Detection

• ETHZ Pedestrian Detection

Action Recognition / Video-based

• UCF Sports Action Data Set

Action Recognition / Image Deblurring

• Levin dataset

Resource link collection / Image Deblurring

• Resource collection - Tao Xie (UIUC) and Yuan Xie (UCSB)

Writing / Image Deblurring

• Common mistakes in technical writing - Wojciech Jarosz (Dartmouth College)

Songs / Image Deblurring

• Machine Learning A Cappella - Overfitting Thriller

Computer Vision

• EENG 512 / CSCI 512 - Computer Vision - William Hoff (Colorado School of Mines)

Blogs / Image Deblurring

• Learn OpenCV - Satya Mallick

• Tombone's Computer Vision Blog - Tomasz Malisiewicz

• Computer vision for dummies - Vincent Spruyt

• Andrej Karpathy blog - Andrej Karpathy

Contour Detection and Image Segmentation / Edge-preserving image processing

• Grab Cut

• Multiscale Combinatorial Grouping

• Fast Edge Detection Using Structured Forests (⭐773)

Object Detection / Localization & Mapping:

• BING: Objectness Estimation

• Edge Boxes (⭐773)

Computer Vision

• Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision

Deep Learning

• 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)

General Purpose Computer Vision Library

• PCL: Point Cloud Library

• ImageUtilities

Multiple-view Computer Vision

• 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

Simultaneous localization and mapping / SLAM community:

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

Simultaneous localization and mapping / Tracking/Odometry:

• PTAM: Parallel tracking and mapping

• InfiniTAM: Implementation of multi-platform large-scale depth tracking and fusion

• VoxelHashing: Large-scale KinectFusion (⭐562)

• SLAMBench: Multiple-implementation of KinectFusion

• SVO: Semi-direct visual odometry (⭐1.9k)

• DVO: dense visual odometry (⭐579)

• FOVIS: RGB-D visual odometry

Simultaneous localization and mapping / Graph Optimization:

• GTSAM: General smoothing and mapping library for Robotics and SFM -- Georgia Institute of Technology

• G2O: General framework for graph optomization (⭐2.4k)

Simultaneous localization and mapping / Loop Closure:

• DBoW2: binary bag-of-words loop detection system

Simultaneous localization and mapping / Localization & Mapping:

• ORB-SLAM (⭐1.3k)

Optimization / Nearest Neighbor Field Estimation

• 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

Recent Conference Talks

• CVPR 2015 - Jun 2015

Low-level Vision / Optical Flow

• 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.

Links / Image Deblurring

• German Computer Vision Research Groups & Companies

Computational Photography

• Revealing the Invisible - Frédo Durand (MIT) 2012

Machine Learning / Nearest Neighbor Field Estimation

• Awesome Machine Learning (⭐56k)

• Bob: a free signal processing and machine learning toolbox for researchers

Low-level Vision / 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

OpenCV Programming

• OpenCV Essentials - Oscar Deniz Suarez, Mª del Milagro Fernandez Carrobles, Noelia Vallez Enano, Gloria Bueno Garcia, Ismael Serrano Gracia

Feature Detection and Extraction

• Local Binary Patterns (⭐92)

Intrinsic Images / Edge-preserving image processing

• Recovering Intrinsic Images with a global Sparsity Prior on Reflectance

• Intrinsic Images by Clustering

Contour Detection and Image Segmentation / Edge-preserving image processing

• SEEDS

Video Segmentation / Edge-preserving image processing

• Streaming hierarchical video segmentation

OpenCV Programming

• Practical Python and OpenCV - Adrian Rosebrock

Machine Learning

• Bayesian Reasoning and Machine Learning - David Barber, Cambridge University Press, 2012

Machine Learning and Statistical Learning

• Methods for Applied Statistics: Unsupervised Learning - Lester Mackey (Stanford)

• Machine Learning - Andrew Zisserman (University of Oxford)

Feature Detection and Extraction

• SIFT++

Visual Tracking / Nearest Neighbor Field Estimation

• Extended Lucas-Kanade Tracking

• Spatio-Temporal Context Learning

• Locality Sensitive Histograms

Intrinsic Images / Image Super-resolutions

• Intrinsic Image Evaluation on Synthetic Complex Scenes

Fundamentals

• Linear Algebra and Its Applications - Gilbert Strang 1995

Conference papers on the web

• 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

Computer Vision

• 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

• ECCV 2014 - Sep 2014

• CVPR 2014 - Jun 2014

• ICCV 2013 - Dec 2013

• CVPR 2013 - Jun 2013

• ECCV 2012 - Oct 2012

• CVPR 2012 - Jun 2012

3D Computer Vision

• Reconstructing the World from Photos on the Internet - Steve Seitz (University of Washington) 2013

Machine Learning

• Bayesian or Frequentist, Which Are You? - Michael I. Jordan (UC Berkeley)

Optimization

• 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

• Deep Learning for Computer Vision - Rob Fergus (NYU/Facebook Research)

• High-dimensional learning with deep network contractions - Stéphane Mallat (Ecole Normale Superieure)

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)

General Purpose Computer Vision Library

• Open CV

• Open source Python module for computer vision (⭐1.9k)

• ccv: A Modern Computer Vision Library (⭐6.9k)

• Matlab Computer Vision System Toolbox

Multiple-view Computer Vision

• MATLAB Functions for Multiple View Geometry

Feature Detection and Extraction

• SIFT
  • David G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, 60, 2 (2004), pp. 91-110.

• 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

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

Low-level Vision / 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)

Low-level Vision / Image Deblurring

• 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

Low-level Vision / Alpha Matting

• Closed-form image matting

• Spectral Matting

• Learning-based Matting

• Improving Image Matting using Comprehensive Sampling Sets

Low-level Vision / Image Pyramid

• The Steerable Pyramid

• CurveLab

Contour Detection and Image Segmentation / Edge-preserving image processing

• Entropy Rate Superpixel

Interactive Image Segmentation / Edge-preserving image processing

• Random Walker

• Geodesic Segmentation

• Lazy Snapping

• Power Watershed

• Geodesic Graph Cut

• Segmentation by Transduction

Video Segmentation / Edge-preserving image processing

• Video Segmentation with Superpixels

• Efficient hierarchical graph-based video segmentation

• Object segmentation in video

Simultaneous localization and mapping / SLAM community:

• openSLAM

Simultaneous localization and mapping / Localization & Mapping:

• RatSLAM

Single-view Spatial Understanding / Localization & Mapping:

• Geometric Context - Derek Hoiem (CMU)

• Recovering Spatial Layout - Varsha Hedau (UIUC)

• Geometric Reasoning - David C. Lee (CMU)

• RGBD2Full3D (⭐23) - Ruiqi Guo (UIUC)

Object Detection / Localization & Mapping:

• Histograms of Sparse Codes for Object Detection

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 Search / Nearest Neighbor Field Estimation

• PatchMatch

• Generalized PatchMatch

• Coherency Sensitive Hashing

• PMBP: PatchMatch Belief Propagation (⭐27)

• TreeCANN

Visual Tracking / Image Super-resolutions

• Visual Tracker Benchmark

• Visual Tracker Benchmark v1.1

• VOT Challenge

• Princeton Tracking Benchmark

External Dataset Link Collection / Nearest Neighbor Field Estimation

• 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 / Image Super-resolutions

• Single-Image Super-Resolution: A Benchmark

Intrinsic Images / Image Super-resolutions

• Ground-truth dataset and baseline evaluations for intrinsic image algorithms

• Intrinsic Images in the Wild

Material Recognition / Image Super-resolutions

• OpenSurface

• Flickr Material Database

Multi-view Reconsturction / Image Super-resolutions

• Multi-View Stereo Reconstruction

Visual Surveillance / Image Super-resolutions

• VIRAT

• CAM2

Change detection / Image Super-resolutions

• ChangeDetection.net

Visual Recognition / Object Detection

• The PASCAL Visual Object Classes

• ImageNet Object Detection Challenge

Visual Recognition / Image Classification

• ImageNet Large Scale Visual Recognition Challenge

Visual Recognition / Scene Recognition

• SUN Database

• Place Dataset

Image Captioning / Image Deblurring

• Microsoft COCO

• Flickr 8K

• Flickr 30K

Visual Recognition / Semantic labeling

• Stanford background dataset

• CamVid

• Barcelona Dataset

• SIFT Flow Dataset

Visual Recognition / Multi-view Object Detection

• 3D Object Dataset

• EPFL Car Dataset

• KTTI Dection Dataset

• SUN 3D Dataset

• PASCAL 3D+

• NYU Car Dataset

Visual Recognition / Fine-grained Visual Recognition

• Fine-grained Classification Challenge

• Caltech-UCSD Birds 200

Visual Recognition / Pedestrian Detection

• Caltech Pedestrian Detection Benchmark

Action Recognition / Video-based

• HOLLYWOOD2 Dataset

Action Recognition / Image Deblurring

• Sun dataset

Resource link collection / Image Deblurring

• 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)

Writing / Image Deblurring

• 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)

Presentation / Image Deblurring

• 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 / Image Deblurring

• 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 / Image Deblurring

• Time Management - Randy Pausch (CMU)

Links / Image Deblurring

• The Computer Vision Industry - David Lowe

• Cat Paper Collection

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

• Visual Object and Activity Recognition - Alexei A. Efros and Trevor Darrell (UC Berkeley)

• Computer Vision - Steve Seitz (University of Washington)

• 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)

• The Three R's of Computer Vision - Jitendra Malik (UC Berkeley) 2013

• Applications to Machine Vision - Andrew Blake (Microsoft Research) 2008

OpenCV Programming

• Learning OpenCV: Computer Vision with the OpenCV Library - Gary Bradski and Adrian Kaehler

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

• Learning From Data- Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin 2012

• Neural Networks and Deep Learning - Michael Nielsen 2014

• Introduction To Bayesian Inference - Christopher Bishop (Microsoft Research) 2009

• Support Vector Machines - Chih-Jen Lin (National Taiwan University) 2006

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)

• 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)

• 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

Machine Learning and Statistical Learning

• 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)

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)

• Optimization Algorithms in Machine Learning - Stephen J. Wright (University of Wisconsin-Madison)

• Convex Optimization - Lieven Vandenberghe (University of California, Los Angeles)

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)

3D Computer Vision

• 3D Computer Vision: Past, Present, and Future - Steve Seitz (University of Washington) 2011

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

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

• 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

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

General Purpose Computer Vision Library

• SimpleCV

• Piotr's Computer Vision Matlab Toolbox

Feature Detection and Extraction

• VLFeat

Multiple-view Computer Vision

• OpenGV - geometric computer vision algorithms

• Visual SFM

• Bundler SFM

• Floating Scale Surface Reconstruction

Low-level Vision / Optical Flow

• Secrets of Optical Flow Estimation and Their Principles

Low-level Vision / Image Completion

• GIMP Resynthesizer

• Priority BP

• PlanarStructureCompletion

Low-level Vision / Alpha Matting

• Alpha Matting Evaluation

Low-level Vision / 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

Contour Detection and Image Segmentation / Edge-preserving image processing

• Mean Shift Segmentation

• Graph-based Segmentation

• Normalized Cut

• Contour Detection and Image Segmentation

• Structured Edge Detection

• Pointwise Mutual Information

• QuickShift

• TurboPixels

Camera calibration / Edge-preserving image processing

• Camera Calibration Toolbox for Matlab

• Camera calibration With OpenCV

• Multiple Camera Calibration Toolbox

Object Detection / Localization & Mapping:

• INRIA Object Detection and Localization Toolkit

• Discriminatively trained deformable part models

• VOC-DPM (⭐573)

• R-CNN: Regions with Convolutional Neural Network Features (⭐2.3k)

• SPP-Net (⭐363)

Visual Tracking / Nearest Neighbor Field Estimation

• Visual Tracking Challenge

• Kanade-Lucas-Tomasi Feature Tracker

Songs / Image Deblurring

• The Fundamental Matrix Song

• The RANSAC Song