awesome-computer-vision
by jbhuang0604
A curated list of awesome computer vision resources
AI summary
Computer vision resources
A curated collection of resources and references for computer vision development
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What's in the list
514 links in 13 sections, with live GitHub stats.activeno commit in 2y
Awesome Lists
Awesome Machine Learning
Awesome Deep Vision
Awesome Domain Adaptation
Awesome Object Detection
Awesome 3D Machine Learning
Awesome Action Recognition
Awesome Scene Understanding
Awesome Adversarial Machine Learning
Awesome Adversarial Deep Learning
Awesome Face
Awesome Face Recognition
Awesome Human Pose Estimation
Awesome medical imaging
Awesome Images
Awesome Graphics
Awesome Neural Radiance Fields
Awesome Implicit Neural Representations
Awesome Neural Rendering
Awesome Public Datasets
Awesome Dataset Tools
Awesome Robotics Datasets
Awesome Explainable AI
Awesome Fairness in AI
Awesome Machine Learning Interpretability
Awesome Production Machine Learning
Awesome Video Text Retrieval
Awesome Image Inpainting
Awesome Deep HDR
Awesome Video Generation
Awesome GAN applications
Awesome Generative Modeling
Awesome Image Classification
Awesome Deep Learning
Awesome Machine Learning in Biomedical(Healthcare) Imaging
Awesome Deep Learning for Tracking and Detection
Awesome Deep Learning for Video Analysis
Awesome Vision + Language
Awesome Visual Transformer
Awesome Embodied Vision
Awesome Anomaly Detection
Awesome Makeup Transfer
Awesome Learning with Label NoiseBooks
- 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
- Image Processing and Analysis
Stan Birchfield 2018
- Computer Vision, From 3D Reconstruction to Recognition
Silvio Savarese 2018
- Learning OpenCV: Computer Vision with the OpenCV Library
Gary Bradski and Adrian Kaehler
- Practical Python and OpenCV
Adrian Rosebrock
- OpenCV Essentials
Oscar Deniz Suarez, Mª del Milagro Fernandez Carrobles, Noelia Vallez Enano, Gloria Bueno Garcia, Ismael Serrano Gracia
- Pattern Recognition and Machine Learning
Christopher M. Bishop 2007
- Neural Networks for Pattern Recognition
Christopher M. Bishop 1995
- Probabilistic Graphical Models: Principles and Techniques
Daphne Koller and Nir Friedman 2009
- Pattern Classification
Peter E. Hart, David G. Stork, and Richard O. Duda 2000
- Machine Learning
Tom M. Mitchell 1997
- Gaussian processes for machine learning
Carl Edward Rasmussen and Christopher K. I. Williams 2005
- Learning From Data
Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin 2012
- Neural Networks and Deep Learning
Michael Nielsen 2014
- Bayesian Reasoning and Machine Learning
David Barber, Cambridge University Press, 2012
- Linear Algebra and Its Applications
Gilbert Strang 1995
Courses
- EENG 512 / CSCI 512 - Computer Vision
William Hoff (Colorado School of Mines)
- Visual Object and Activity Recognition
Alexei A. Efros and Trevor Darrell (UC Berkeley)
- Computer Vision
Steve Seitz (University of Washington)
- Spring 2016
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)
- 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):
- Image Manipulation and Computational Photography
Alexei A. Efros (UC Berkeley)
- Computational Photography
Alexei A. Efros (CMU)
- Computational Photography
Derek Hoiem (UIUC)
- Computational Photography
James Hays (Brown University)
- Digital & Computational Photography
Fredo Durand (MIT)
- Computational Camera and Photography
Ramesh Raskar (MIT Media Lab)
- Computational Photography
Irfan Essa (Georgia Tech)
- Courses in Graphics
Stanford University
- Computational Photography
Rob Fergus (NYU)
- Introduction to Visual Computing
Kyros Kutulakos (University of Toronto)
- Computational Photography
Kyros Kutulakos (University of Toronto)
- Computer Vision for Visual Effects
Rich Radke (Rensselaer Polytechnic Institute)
- Introduction to Image Processing
Rich Radke (Rensselaer Polytechnic Institute)
- Machine Learning
Andrew Ng (Stanford University)
- Learning from Data
Yaser S. Abu-Mostafa (Caltech)
- Statistical Learning
Trevor Hastie and Rob Tibshirani (Stanford University)
- Statistical Learning Theory and Applications
Tomaso Poggio, Lorenzo Rosasco, Carlo Ciliberto, Charlie Frogner, Georgios Evangelopoulos, Ben Deen (MIT)
- Statistical Learning
Genevera Allen (Rice University)
- Practical Machine Learning
Michael Jordan (UC Berkeley)
- Course on Information Theory, Pattern Recognition, and Neural Networks
David MacKay (University of Cambridge)
- Methods for Applied Statistics: Unsupervised Learning
Lester Mackey (Stanford)
- Machine Learning
Andrew Zisserman (University of Oxford)
- Intro to Machine Learning
Sebastian Thrun (Stanford University)
- Machine Learning
Charles Isbell, Michael Littman (Georgia Tech)
- (Convolutional) Neural Networks for Visual Recognition
Fei-Fei Li, Andrej Karphaty, Justin Johnson (Stanford University)
- Machine Learning for Computer Vision
Rudolph Triebel (TU Munich)
- 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
- CVPapers
Computer vision papers on the web
- SIGGRAPH Paper on the web
Graphics papers on the web
- NIPS Proceedings
NIPS papers on the web
- Annotated Computer Vision Bibliography
Keith Price (USC)
Pre-trained Computer Vision Models
List of Computer Vision models
These models are trained on custom objects
Tutorials and talks
- Computer Vision Talks
Lectures, keynotes, panel discussions on computer vision
- The Three R's of Computer Vision
Jitendra Malik (UC Berkeley) 2013
- Applications to Machine Vision
Andrew Blake (Microsoft Research) 2008
- The Future of Image Search
Jitendra Malik (UC Berkeley) 2008
- Should I do a PhD in Computer Vision?
Fatih Porikli (Australian National University)
- CVPR 2015
Jun 2015
- ECCV 2014
Sep 2014
- CVPR 2014
Jun 2014
- ICCV 2013
Dec 2013
- ICML 2013
Jul 2013
- CVPR 2013
Jun 2013
- ECCV 2012
Oct 2012
- ICML 2012
Jun 2012
- CVPR 2012
Jun 2012
- 3D Computer Vision: Past, Present, and Future
Steve Seitz (University of Washington) 2011
- Reconstructing the World from Photos on the Internet
Steve Seitz (University of Washington) 2013
- The Distributed Camera
Noah Snavely (Cornell University) 2011
- Planet-Scale Visual Understanding
Noah Snavely (Cornell University) 2014
- A Trillion Photos
Steve Seitz (University of Washington) 2013
- Reflections on Image-Based Modeling and Rendering
Richard Szeliski (Microsoft Research) 2013
- Photographing Events over Time
William T. Freeman (MIT) 2011
- Old and New algorithm for Blind Deconvolution
Yair Weiss (The Hebrew University of Jerusalem) 2011
- A Tour of Modern "Image Processing"
Peyman Milanfar (UC Santa Cruz/Google) 2010
- Topics in image and video processing
Andrew Blake (Microsoft Research) 2007
- Computational Photography
William T. Freeman (MIT) 2012
- Revealing the Invisible
Frédo Durand (MIT) 2012
- Overview of Computer Vision and Visual Effects
Rich Radke (Rensselaer Polytechnic Institute) 2014
- Where machine vision needs help from machine learning
William T. Freeman (MIT) 2011
- Learning in Computer Vision
Simon Lucey (CMU) 2008
- Learning and Inference in Low-Level Vision
Yair Weiss (The Hebrew University of Jerusalem) 2009
- Object Recognition
Larry Zitnick (Microsoft Research)
- Generative Models for Visual Objects and Object Recognition via Bayesian Inference
Fei-Fei Li (Stanford University)
- Graphical Models for Computer Vision
Pedro Felzenszwalb (Brown University) 2012
- Graphical Models
Zoubin Ghahramani (University of Cambridge) 2009
- Machine Learning, Probability and Graphical Models
Sam Roweis (NYU) 2006
- Graphical Models and Applications
Yair Weiss (The Hebrew University of Jerusalem) 2009
- A Gentle Tutorial of the EM Algorithm
Jeff A. Bilmes (UC Berkeley) 1998
- Introduction To Bayesian Inference
Christopher Bishop (Microsoft Research) 2009
- Support Vector Machines
Chih-Jen Lin (National Taiwan University) 2006
- Bayesian or Frequentist, Which Are You?
Michael I. Jordan (UC Berkeley)
- Optimization Algorithms in Machine Learning
Stephen J. Wright (University of Wisconsin-Madison)
- Convex Optimization
Lieven Vandenberghe (University of California, Los Angeles)
- Continuous Optimization in Computer Vision
Andrew Fitzgibbon (Microsoft Research)
- Beyond stochastic gradient descent for large-scale machine learning
Francis Bach (INRIA)
- Variational Methods for Computer Vision
Daniel Cremers (Technische Universität München) ( )
- A tutorial on Deep Learning
Geoffrey E. Hinton (University of Toronto)
- Deep Learning
Ruslan Salakhutdinov (University of Toronto)
- Scaling up Deep Learning
Yoshua Bengio (University of Montreal)
- ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky (University of Toronto)
- The Unreasonable Effectivness Of Deep Learning
Yann LeCun (NYU/Facebook Research) 2014
- Deep Learning for Computer Vision
Rob Fergus (NYU/Facebook Research)
- High-dimensional learning with deep network contractions
Stéphane Mallat (Ecole Normale Superieure)
- Machine Learning Summer School
Reykjavik, Iceland 2014
Tutorials and talks / Machine Learning Summer School
- Deep Learning Session 1
Yoshua Bengio (Universtiy of Montreal)
- Deep Learning Session 2
Yoshua Bengio (University of Montreal)
- Deep Learning Session 3
Yoshua Bengio (University of Montreal)
Software
- Computer Vision Resources
Jia-Bin Huang (UIUC)
- Source Code Collection for Reproducible Research
Xin Li (West Virginia University)
- OpenGV
geometric computer vision algorithms
- MinimalSolvers
Minimal problems solver
- openMVG: open Multiple View Geometry
Multiple View Geometry; Structure from Motion library & softwares
- Coarse2Fine Optical Flow
Ce Liu (MIT)
- GTSAM: General smoothing and mapping library for Robotics and SFM
-- Georgia Institute of Technology
- FabMap: appearance-based loop closure system
also available in
- Geometric Context
Derek Hoiem (CMU)
- Recovering Spatial Layout
Varsha Hedau (UIUC)
- Geometric Reasoning
David C. Lee (CMU)
RGBD2Full3D
Ruiqi Guo (UIUC)
- 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
gtmaker
Open source Python module for computer vision
ccv: A Modern Computer Vision Library
Awesome 3D reconstruction list
Local Binary Patterns
HDR_Toolbox
SEEDS Revised
Fast Edge Detection Using Structured Forests
KFusion: Implementation of KinectFusion
kinfu_remake: Lightweight, reworked and optimized version of Kinfu.
VoxelHashing: Large-scale KinectFusion
SVO: Semi-direct visual odometry
DVO: dense visual odometry
G2O: General framework for graph optomization
ORB-SLAM
VOC-DPM
SPP-Net
PMBP: PatchMatch Belief Propagation
NeuralTalkDatasets
- CV Datasets on the web
CVPapers
- Are we there yet?
Which paper provides the best results on standard dataset X?
PASS: An An ImageNet replacement for self-supervised pretraining without humansAerial Image Segmentation - Learning Aerial Image Segmentation From Online Maps / Resources for students
- Resources for students
Frédo Durand (MIT)
- Advice for Graduate Students
Aaron Hertzmann (Adobe Research)
- Graduate Skills Seminars
Yashar Ganjali, Aaron Hertzmann (University of Toronto)
- Research Skills
Simon Peyton Jones (Microsoft Research)
- Resource collection
Tao Xie (UIUC) and Yuan Xie (UCSB)
- Write Good Papers
Frédo Durand (MIT)
- Notes on writing
Frédo Durand (MIT)
- How to Write a Bad Article
Frédo Durand (MIT)
- How to write a good CVPR submission
William T. Freeman (MIT)
- How to write a great research paper
Simon Peyton Jones (Microsoft Research)
- How to write a SIGGRAPH paper
SIGGRAPH ASIA 2011 Course
- Writing Research Papers
Aaron Hertzmann (Adobe Research)
- How to Write a Paper for SIGGRAPH
Jim Blinn
- How to Get Your SIGGRAPH Paper Rejected
Jim Kajiya (Microsoft Research)
- How to write a SIGGRAPH paper
Li-Yi Wei (The University of Hong Kong)
- How to Write a Great Paper
Martin Martin Hering Hering--Bertram (Hochschule Bremen University of Applied Sciences)
- How to have a paper get into SIGGRAPH?
Takeo Igarashi (The University of Tokyo)
- Good Writing
Marc H. Raibert (Boston Dynamics, Inc.)
- How to Write a Computer Vision Paper
Derek Hoiem (UIUC)
- Common mistakes in technical writing
Wojciech Jarosz (Dartmouth College)
- Giving a Research Talk
Frédo Durand (MIT)
- How to give a good talk
David Fleet (University of Toronto) and Aaron Hertzmann (Adobe Research)
- Designing conference posters
Colin Purrington
- How to do research
William T. Freeman (MIT)
- You and Your Research
Richard Hamming
- Seven Warning Signs of Bogus Science
Robert L. Park
- Five Principles for Choosing Research Problems in Computer Graphics
Thomas Funkhouser (Cornell University)
- How To Do Research In the MIT AI Lab
David Chapman (MIT)
- Recent Advances in Computer Vision
Ming-Hsuan Yang (UC Merced)
- How to Come Up with Research Ideas in Computer Vision?
Jia-Bin Huang (UIUC)
- How to Read Academic Papers
Jia-Bin Huang (UIUC)
- Time Management
Randy Pausch (CMU)
Aerial Image Segmentation - Learning Aerial Image Segmentation From Online Maps / Blogs
- Learn OpenCV
Satya Mallick
- Tombone's Computer Vision Blog
Tomasz Malisiewicz
- Computer vision for dummies
Vincent Spruyt
- Andrej Karpathy blog
Andrej Karpathy
- AI Shack
Utkarsh Sinha
- Computer Vision Talks
Eugene Khvedchenya
Computer Vision Basics with Python Keras and OpenCV
Jason Chin (University of Western Ontario)
Aerial Image Segmentation - Learning Aerial Image Segmentation From Online Maps / Links
- The Computer Vision Industry
David Lowe
Aerial Image Segmentation - Learning Aerial Image Segmentation From Online Maps / Songs
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