Awesome Computer Vision: / Awesome Lists |
Awesome Machine Learning | 65,671 | 2 months ago | |
Awesome Deep Vision | 10,789 | about 1 year ago | |
Awesome Domain Adaptation | 5,076 | 25 days ago | |
Awesome Object Detection | 7,387 | almost 2 years ago | |
Awesome 3D Machine Learning | 9,707 | 3 months ago | |
Awesome Action Recognition | 3,788 | over 1 year ago | |
Awesome Scene Understanding | 697 | 3 months ago | |
Awesome Adversarial Machine Learning | 1,803 | almost 4 years ago | |
Awesome Adversarial Deep Learning | 261 | over 3 years ago | |
Awesome Face | 892 | about 5 years ago | |
Awesome Face Recognition | 4,493 | over 1 year ago | |
Awesome Human Pose Estimation | 1,330 | about 4 years ago | |
Awesome medical imaging | 202 | over 4 years ago | |
Awesome Images | 2,433 | about 3 years ago | |
Awesome Graphics | 1,049 | over 4 years ago | |
Awesome Neural Radiance Fields | 6,469 | 3 months ago | |
Awesome Implicit Neural Representations | 2,450 | 8 months ago | |
Awesome Neural Rendering | 2,294 | 3 months ago | |
Awesome Public Datasets | 60,356 | about 1 month ago | |
Awesome Dataset Tools | 841 | over 1 year ago | |
Awesome Robotics Datasets | 360 | about 3 years ago | |
Awesome Mobile Machine Learning | | | |
Awesome Explainable AI | 1,404 | 6 days ago | |
Awesome Fairness in AI | 311 | about 1 year ago | |
Awesome Machine Learning Interpretability | 3,630 | 5 days ago | |
Awesome Production Machine Learning | 17,427 | 12 days ago | |
Awesome Video Text Retrieval | 585 | 12 months ago | |
Awesome Image-to-Image Translation | 1,169 | about 1 month ago | |
Awesome Image Inpainting | 1,858 | 2 months ago | |
Awesome Deep HDR | 391 | 4 months ago | |
Awesome Video Generation | 74 | about 4 years ago | |
Awesome GAN applications | 4,955 | about 1 year ago | |
Awesome Generative Modeling | 157 | over 3 years ago | |
Awesome Image Classification | 2,817 | over 2 years ago | |
Awesome Deep Learning | 23,863 | 6 months ago | |
Awesome Machine Learning in Biomedical(Healthcare) Imaging | 61 | almost 5 years ago | |
Awesome Deep Learning for Tracking and Detection | 2,429 | 5 months ago | |
Awesome Human Pose Estimation | 1,330 | about 4 years ago | |
Awesome Deep Learning for Video Analysis | 755 | about 3 years ago | |
Awesome Vision + Language | 1,138 | about 2 years ago | |
Awesome Robotics | 4,272 | 20 days ago | |
Awesome Visual Transformer | 3,362 | over 1 year ago | |
Awesome Embodied Vision | 505 | 3 months ago | |
Awesome Anomaly Detection | 2,733 | about 2 years ago | |
Awesome Makeup Transfer | 211 | 6 months ago | |
Awesome Learning with Label Noise | 2,628 | 5 months ago | |
Awesome Deblurring | 2,407 | 5 months ago | |
Awsome Deep Geometry Learning | 340 | about 3 years ago | |
Awesome Image Distortion Correction | 235 | over 1 year ago | |
Awesome Neuron Segmentation in EM Images | 45 | 7 months ago | |
Awsome Delineation | 21 | over 3 years ago | |
Awesome ImageHarmonization | 18 | almost 4 years ago | |
Awsome GAN Training | 27 | almost 4 years ago | |
Awesome Document Understanding | 1,282 | over 1 year 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 | 60 | over 2 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 | 11 | almost 4 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 | 1,919 | almost 4 years ago | |
ccv: A Modern Computer Vision Library | 7,078 | 14 days 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 | 4,148 | about 3 years ago | |
VLFeat | | | |
SIFT | | | |
SIFT++ | | | |
BRISK | | | |
SURF | | | |
FREAK | | | |
AKAZE | | | |
Local Binary Patterns | 97 | almost 7 years ago | |
HDR_Toolbox | 372 | about 2 months 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 | 52 | almost 6 years ago | |
Multiscale Combinatorial Grouping | | | |
Fast Edge Detection Using Structured Forests | 818 | almost 5 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 9 years ago | |
kinfu_remake: Lightweight, reworked and optimized version of Kinfu. | 342 | over 5 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 | 668 | almost 4 years ago | |
SLAMBench: Multiple-implementation of KinectFusion | | | |
SVO: Semi-direct visual odometry | 2,088 | about 5 years ago | |
DVO: dense visual odometry | 636 | about 8 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 | 3,061 | 5 days ago | |
FabMap: appearance-based loop closure system | | | also available in |
DBoW2: binary bag-of-words loop detection system | | | |
RatSLAM | | | |
LSD-SLAM | 2,607 | over 1 year ago | |
ORB-SLAM | 1,510 | about 2 years ago | |
Geometric Context | | | Derek Hoiem (CMU) |
Recovering Spatial Layout | | | Varsha Hedau (UIUC) |
Geometric Reasoning | | | David C. Lee (CMU) |
RGBD2Full3D | 24 | about 10 years ago | Ruiqi Guo (UIUC) |
INRIA Object Detection and Localization Toolkit | | | |
Discriminatively trained deformable part models | | | |
VOC-DPM | 577 | over 7 years ago | |
Histograms of Sparse Codes for Object Detection | | | |
R-CNN: Regions with Convolutional Neural Network Features | 2,368 | over 7 years ago | |
SPP-Net | 364 | over 8 years ago | |
BING: Objectness Estimation | | | |
Edge Boxes | 818 | almost 5 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 10 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 | 5,407 | almost 4 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 | 10,789 | about 1 year ago | |
Awesome Machine Learning | 65,671 | 2 months 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 2 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 | 429 | over 3 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 | 23,863 | 6 months ago | |
awesome-machine-learning | 65,671 | 2 months 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 | | | |