Awesome Computer Vision: / Awesome Lists |
| Awesome Machine Learning | 66,380 | almost 2 years ago | |
| Awesome Deep Vision | 10,845 | about 3 years ago | |
| Awesome Domain Adaptation | 5,146 | almost 2 years ago | |
| Awesome Object Detection | 7,429 | almost 4 years ago | |
| Awesome 3D Machine Learning | 9,813 | about 2 years ago | |
| Awesome Action Recognition | 3,830 | over 3 years ago | |
| Awesome Scene Understanding | 731 | almost 2 years ago | |
| Awesome Adversarial Machine Learning | 1,819 | almost 6 years ago | |
| Awesome Adversarial Deep Learning | 263 | over 5 years ago | |
| Awesome Face | 897 | about 7 years ago | |
| Awesome Face Recognition | 4,543 | over 3 years ago | |
| Awesome Human Pose Estimation | 1,341 | about 6 years ago | |
| Awesome medical imaging | 209 | over 6 years ago | |
| Awesome Images | 2,446 | about 5 years ago | |
| Awesome Graphics | 1,060 | over 6 years ago | |
| Awesome Neural Radiance Fields | 6,545 | almost 2 years ago | |
| Awesome Implicit Neural Representations | 2,479 | over 2 years ago | |
| Awesome Neural Rendering | 2,308 | almost 2 years ago | |
| Awesome Public Datasets | 61,377 | almost 2 years ago | |
| Awesome Dataset Tools | 859 | over 3 years ago | |
| Awesome Robotics Datasets | 382 | about 5 years ago | |
| Awesome Mobile Machine Learning | | | |
| Awesome Explainable AI | 1,438 | almost 2 years ago | |
| Awesome Fairness in AI | 316 | about 3 years ago | |
| Awesome Machine Learning Interpretability | 3,687 | almost 2 years ago | |
| Awesome Production Machine Learning | 17,721 | almost 2 years ago | |
| Awesome Video Text Retrieval | 598 | almost 3 years ago | |
| Awesome Image-to-Image Translation | 1,190 | almost 2 years ago | |
| Awesome Image Inpainting | 1,933 | almost 2 years ago | |
| Awesome Deep HDR | 397 | about 2 years ago | |
| Awesome Video Generation | 76 | about 6 years ago | |
| Awesome GAN applications | 5,003 | about 3 years ago | |
| Awesome Generative Modeling | 157 | over 5 years ago | |
| Awesome Image Classification | 2,870 | over 4 years ago | |
| Awesome Deep Learning | 24,435 | over 2 years ago | |
| Awesome Machine Learning in Biomedical(Healthcare) Imaging | 62 | almost 7 years ago | |
| Awesome Deep Learning for Tracking and Detection | 2,441 | over 2 years ago | |
| Awesome Human Pose Estimation | 1,341 | about 6 years ago | |
| Awesome Deep Learning for Video Analysis | 767 | almost 5 years ago | |
| Awesome Vision + Language | 1,147 | about 4 years ago | |
| Awesome Robotics | 4,444 | almost 2 years ago | |
| Awesome Visual Transformer | 3,406 | over 3 years ago | |
| Awesome Embodied Vision | 539 | almost 2 years ago | |
| Awesome Anomaly Detection | 2,758 | about 4 years ago | |
| Awesome Makeup Transfer | 219 | almost 2 years ago | |
| Awesome Learning with Label Noise | 2,647 | over 2 years ago | |
| Awesome Deblurring | 2,474 | over 2 years ago | |
| Awsome Deep Geometry Learning | 346 | about 5 years ago | |
| Awesome Image Distortion Correction | 242 | about 3 years ago | |
| Awesome Neuron Segmentation in EM Images | 46 | over 2 years ago | |
| Awsome Delineation | 22 | over 5 years ago | |
| Awesome ImageHarmonization | 18 | almost 6 years ago | |
| Awsome GAN Training | 27 | almost 6 years ago | |
| Awesome Document Understanding | 1,330 | over 3 years ago | |
Awesome Computer Vision: / Books |
| 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 |
| Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics | | | Justin Solomon 2015 |
| 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 |
Awesome Computer Vision: / 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) |
Awesome Computer Vision: / 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 |
| Computer Vision Foundation open access | | | |
| Annotated Computer Vision Bibliography | | | Keith Price (USC) |
| Calendar of Computer Image Analysis, Computer Vision Conferences | | | (USC) |
| Visionbib Survey Paper List | | | |
| Foundations and Trends® in Computer Graphics and Vision | | | |
| Computer Vision: A Reference Guide | | | |
Awesome Computer Vision: / Pre-trained Computer Vision Models |
| List of Computer Vision models | 62 | over 4 years ago | These models are trained on custom objects |
Awesome Computer Vision: / 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) |
| Graduate Summer School 2013: Computer Vision | | | IPAM, 2013 |
| 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) |
| 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 |
Awesome Computer Vision: / 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) |
Awesome Computer Vision: / Software |
| Comma Coloring | | | |
| Annotorious | | | |
| LabelME | | | |
| gtmaker | 12 | almost 6 years ago | |
| 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 | | | |
| Open CV | | | |
| mexopencv | | | |
| SimpleCV | | | |
| Open source Python module for computer vision | 1,929 | over 5 years ago | |
| ccv: A Modern Computer Vision Library | 7,102 | almost 2 years ago | |
| VLFeat | | | |
| Matlab Computer Vision System Toolbox | | | |
| Piotr's Computer Vision Matlab Toolbox | | | |
| PCL: Point Cloud Library | | | |
| ImageUtilities | | | |
| 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 | | | |
| Awesome 3D reconstruction list | 4,183 | almost 5 years ago | |
| VLFeat | | | |
| SIFT | | | |
| SIFT++ | | | |
| BRISK | | | |
| SURF | | | |
| FREAK | | | |
| AKAZE | | | |
| Local Binary Patterns | 97 | almost 9 years ago | |
| HDR_Toolbox | 374 | about 2 years ago | |
| List of Semantic Segmentation algorithms | | | |
| Middlebury Stereo Vision | | | |
| The KITTI Vision Benchmark Suite | | | |
| LIBELAS: Library for Efficient Large-scale Stereo Matching | | | |
| Ground Truth Stixel Dataset | | | |
| 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. | | | |
| Multi-frame image super-resolution | | | |
| Markov Random Fields for Super-Resolution | | | |
| Sparse regression and natural image prior | | | |
| Single-Image Super Resolution via a Statistical Model | | | |
| Sparse Coding for Super-Resolution | | | |
| Patch-wise Sparse Recovery | | | |
| Neighbor embedding | | | |
| Deformable Patches | | | |
| SRCNN | | | |
| A+: Adjusted Anchored Neighborhood Regression | | | |
| Transformed Self-Exemplars | | | |
| 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 | | | |
| GIMP Resynthesizer | | | |
| Priority BP | | | |
| ImageMelding | | | |
| PlanarStructureCompletion | | | |
| RetargetMe | | | |
| Alpha Matting Evaluation | | | |
| Closed-form image matting | | | |
| Spectral Matting | | | |
| Learning-based Matting | | | |
| Improving Image Matting using Comprehensive Sampling Sets | | | |
| The Steerable Pyramid | | | |
| CurveLab | | | |
| 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 | | | |
| Recovering Intrinsic Images with a global Sparsity Prior on Reflectance | | | |
| Intrinsic Images by Clustering | | | |
| 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 | 53 | almost 8 years ago | |
| Multiscale Combinatorial Grouping | | | |
| Fast Edge Detection Using Structured Forests | 829 | almost 7 years ago | |
| Random Walker | | | |
| Geodesic Segmentation | | | |
| Lazy Snapping | | | |
| Power Watershed | | | |
| Geodesic Graph Cut | | | |
| Segmentation by Transduction | | | |
| Video Segmentation with Superpixels | | | |
| Efficient hierarchical graph-based video segmentation | | | |
| Object segmentation in video | | | |
| Streaming hierarchical video segmentation | | | |
| Camera Calibration Toolbox for Matlab | | | |
| Camera calibration With OpenCV | | | |
| Multiple Camera Calibration Toolbox | | | |
| openSLAM | | | |
| Kitti Odometry: benchmark for outdoor visual odometry (codes may be available) | | | |
| LIBVISO2: C++ Library for Visual Odometry 2 | | | |
| PTAM: Parallel tracking and mapping | | | |
| KFusion: Implementation of KinectFusion | 194 | over 11 years ago | |
| kinfu_remake: Lightweight, reworked and optimized version of Kinfu. | 344 | over 7 years ago | |
| 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 | 673 | almost 6 years ago | |
| SLAMBench: Multiple-implementation of KinectFusion | | | |
| SVO: Semi-direct visual odometry | 2,110 | about 7 years ago | |
| DVO: dense visual odometry | 647 | almost 10 years ago | |
| FOVIS: RGB-D visual odometry | | | |
| GTSAM: General smoothing and mapping library for Robotics and SFM | | | -- Georgia Institute of Technology |
| G2O: General framework for graph optomization | 3,116 | almost 2 years ago | |
| FabMap: appearance-based loop closure system | | | also available in |
| DBoW2: binary bag-of-words loop detection system | | | |
| RatSLAM | | | |
| LSD-SLAM | 2,624 | over 3 years ago | |
| ORB-SLAM | 1,533 | about 4 years ago | |
| Geometric Context | | | Derek Hoiem (CMU) |
| Recovering Spatial Layout | | | Varsha Hedau (UIUC) |
| Geometric Reasoning | | | David C. Lee (CMU) |
| RGBD2Full3D | 24 | about 12 years ago | Ruiqi Guo (UIUC) |
| INRIA Object Detection and Localization Toolkit | | | |
| Discriminatively trained deformable part models | | | |
| VOC-DPM | 578 | over 9 years ago | |
| Histograms of Sparse Codes for Object Detection | | | |
| R-CNN: Regions with Convolutional Neural Network Features | 2,381 | over 9 years ago | |
| SPP-Net | 364 | about 10 years ago | |
| BING: Objectness Estimation | | | |
| Edge Boxes | 829 | almost 7 years ago | |
| ReInspect | | | |
| ANN: A Library for Approximate Nearest Neighbor Searching | | | |
| FLANN - Fast Library for Approximate Nearest Neighbors | | | |
| Fast k nearest neighbor search using GPU | | | |
| PatchMatch | | | |
| Generalized PatchMatch | | | |
| Coherency Sensitive Hashing | | | |
| PMBP: PatchMatch Belief Propagation | 27 | about 12 years ago | |
| TreeCANN | | | |
| 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 | | | |
| NeuralTalk | 5,414 | over 5 years ago | - |
| 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 |
| Awesome Deep Vision | 10,845 | about 3 years ago | |
| Awesome Machine Learning | 66,380 | almost 2 years ago | |
| Bob: a free signal processing and machine learning toolbox for researchers | | | |
| LIBSVM -- A Library for Support Vector Machines | | | |
Awesome Computer Vision: / Datasets |
| 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 | | | |
| VisualData | | | |
| Middlebury Stereo Vision | | | |
| The KITTI Vision Benchmark Suite | | | |
| LIBELAS: Library for Efficient Large-scale Stereo Matching | | | |
| Ground Truth Stixel Dataset | | | |
| Middlebury Optical Flow Evaluation | | | |
| MPI-Sintel Optical Flow Dataset and Evaluation | | | |
| The KITTI Vision Benchmark Suite | | | |
| HCI Challenge | | | |
| DAVIS: Densely Annotated VIdeo Segmentation | | | |
| SegTrack v2 | | | |
| Labeled and Annotated Sequences for Integral Evaluation of SegmenTation Algorithms | | | |
| ChangeDetection.net | | | |
| Single-Image Super-Resolution: A Benchmark | | | |
| Ground-truth dataset and baseline evaluations for intrinsic image algorithms | | | |
| Intrinsic Images in the Wild | | | |
| Intrinsic Image Evaluation on Synthetic Complex Scenes | | | |
| OpenSurface | | | |
| Flickr Material Database | | | |
| Materials in Context Dataset | | | |
| Multi-View Stereo Reconstruction | | | |
| Visual Tracker Benchmark | | | |
| Visual Tracker Benchmark v1.1 | | | |
| VOT Challenge | | | |
| Princeton Tracking Benchmark | | | |
| Tracking Manipulation Tasks (TMT) | | | |
| VIRAT | | | |
| CAM2 | | | |
| ChangeDetection.net | | | |
| The PASCAL Visual Object Classes | | | |
| ImageNet Large Scale Visual Recognition Challenge | | | |
| PASS: An An ImageNet replacement for self-supervised pretraining without humans | 262 | over 4 years ago | |
| SUN Database | | | |
| Place Dataset | | | |
| The PASCAL Visual Object Classes | | | |
| ImageNet Object Detection Challenge | | | |
| Microsoft COCO | | | |
| Stanford background dataset | | | |
| CamVid | | | |
| Barcelona Dataset | | | |
| SIFT Flow Dataset | | | |
| 3D Object Dataset | | | |
| EPFL Car Dataset | | | |
| KTTI Dection Dataset | | | |
| SUN 3D Dataset | | | |
| PASCAL 3D+ | | | |
| NYU Car Dataset | | | |
| Fine-grained Classification Challenge | | | |
| Caltech-UCSD Birds 200 | | | |
| Caltech Pedestrian Detection Benchmark | | | |
| ETHZ Pedestrian Detection | | | |
| HOLLYWOOD2 Dataset | | | |
| UCF Sports Action Data Set | | | |
| Sun dataset | | | |
| Levin dataset | | | |
| Flickr 8K | | | |
| Flickr 30K | | | |
| Microsoft COCO | | | |
Aerial 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 |
| 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 | | | 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 | 432 | over 5 years ago | Jason Chin (University of Western Ontario) |
Aerial Image Segmentation - Learning Aerial Image Segmentation From Online Maps / Links |
| The Computer Vision Industry | | | David Lowe |
| German Computer Vision Research Groups & Companies | | | |
| awesome-deep-learning | 24,435 | over 2 years ago | |
| awesome-machine-learning | 66,380 | almost 2 years ago | |
| Cat Paper Collection | | | |
| Computer Vision News | | | |
Aerial Image Segmentation - Learning Aerial Image Segmentation From Online Maps / Songs |
| The Fundamental Matrix Song | | | |
| The RANSAC Song | | | |
| Machine Learning A Cappella - Overfitting Thriller | | | |
awesome-computer-vision
sindresorhus/awesome
bayandin/awesome-awesomeness
terkelg/awesome-creative-coding
jnv/lists
kiloreux/awesome-robotics
sacridini/awesome-geospatial
jslee02/awesome-robotics-libraries
manfreddiaz/awesome-autonomous-vehicles
eug/awesome-opengl
tstanislawek/awesome-document-understanding
vanrez-nez/awesome-glsl
ahundt/awesome-robotics
szenergy/awesome-lidar
szabgab/awesome-lists
0ex/more-awesome
gorkemalgan/deep_learning_with_noisy_labels_literature
preferredai/cornac
jolibrain/joligen
filipecasal/knowledge-repo
open-mmlab/mmhuman3d
google-research-datasets/dstc8-schema-guided-dialogue
kwea123/nerf_pl
upstageai/evalverse
forcecore/keras-gan-animeface-character