Awesome-People-in-Computer-Vision
https://github.com/solarlee/Awesome-People-in-Computer-Vision
Last synced: 4 days ago
JSON representation
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Contributing
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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
- 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
- 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
- Gaussian processes for machine learning - Carl Edward Rasmussen and Christopher K. I. Williams 2005
- 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
- Vision Science: Photons to Phenomenology - Stephen E. Palmer 1999
- Machine Learning - Tom M. Mitchell 1997
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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)
- Visual Recognition - Kristen Grauman (UT Austin)
- Language and Vision - Tamara Berg (UNC Chapel Hill)
- Convolutional Neural Networks for Visual Recognition - Fei-Fei Li and Andrej Karpathy (Stanford University)
- Computer Vision - Rob Fergus (NYU)
- Computer Vision - Derek Hoiem (UIUC)
- Computer Vision: Foundations and Applications - Kalanit Grill-Spector and Fei-Fei Li (Stanford University)
- High-Level Vision: Behaviors, Neurons and Computational Models - Fei-Fei Li (Stanford University)
- Advances in Computer Vision - Antonio Torralba and Bill Freeman (MIT)
- Computer Vision - Bastian Leibe (RWTH Aachen University)
- Computer Vision 2 - Bastian Leibe (RWTH Aachen University)
- Computational Photography - Alexei A. Efros (CMU)
- 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)
- 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)
- Methods for Applied Statistics: Unsupervised Learning - Lester Mackey (Stanford)
- Machine Learning - Andrew Zisserman (University of Oxford)
- Convex Optimization I - Stephen Boyd (Stanford University)
- Convex Optimization II - Stephen Boyd (Stanford University)
- Convex Optimization - Stephen Boyd (Stanford University)
- Optimization at MIT - (MIT)
- Computational Photography - Derek Hoiem (UIUC)
- Learning from Data - Yaser S. Abu-Mostafa (Caltech)
- Computational Photography - James Hays (Brown University)
- Image Manipulation and Computational Photography - Alexei A. Efros (UC Berkeley)
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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
- Visionbib Survey Paper List
- Foundations and Trends® in Computer Graphics and Vision
- Computer Vision: A Reference Guide
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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
- 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
- Computational Photography - William T. Freeman (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
- Optimization Algorithms in Machine Learning - Stephen J. Wright (University of Wisconsin-Madison)
- Convex Optimization - Lieven Vandenberghe (University of California, Los Angeles)
- Continuous Optimization in Computer Vision - Andrew Fitzgibbon (Microsoft Research)
- Beyond stochastic gradient descent for large-scale machine learning - Francis Bach (INRIA)
- Variational Methods for Computer Vision - Daniel Cremers (Technische Universität München) ([lecture 18 missing from playlist](https://www.youtube.com/watch?v=GgcbVPNd3SI))
- A tutorial on Deep Learning - Geoffrey E. Hinton (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
- Deep Learning for Computer Vision - Rob Fergus (NYU/Facebook Research)
- High-dimensional learning with deep network contractions - Stéphane Mallat (Ecole Normale Superieure)
- Graduate Summer School 2012: Deep Learning, Feature Learning - IPAM, 2012
- Workshop on Big Data and Statistical Machine Learning
- Machine Learning Summer School - Reykjavik, Iceland 2014
- Deep Learning Session 1 - Yoshua Bengio (Universtiy of Montreal)
- Deep Learning Session 2 - Yoshua Bengio (University of Montreal)
- Deep Learning Session 3 - Yoshua Bengio (University of Montreal)
- CVPR 2015 - Jun 2015
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Bayesian or Frequentist, Which Are You? - Michael I. Jordan (UC Berkeley)
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Revealing the Invisible - Frédo Durand (MIT) 2012
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
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Software
- ImageUtilities
- MATLAB Functions for Multiple View Geometry
- 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
- SimpleCV
- VLFeat
- Matlab Computer Vision System Toolbox
- Piotr's Computer Vision Matlab Toolbox
- PCL: Point Cloud Library
- Peter Kovesi's Matlab Functions for Computer Vision and Image Analysis
- 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
- SIFT
- SIFT++
- BRISK
- SURF
- FREAK
- AKAZE
- Spatially variant non-blind deconvolution
- Secrets of Optical Flow Estimation and Their Principles
- 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
- Transformed Self-Exemplars
- 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
- Efficient marginal likelihood optimization in blind deconvolution
- Unnatural L0 Sparse Representation for Natural Image Deblurring
- 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
- 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
- Multiscale Combinatorial Grouping
- 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
- LVR-KinFu: kinfu_remake based Large Scale KinectFusion with online reconstruction
- InfiniTAM: Implementation of multi-platform large-scale depth tracking and fusion
- SLAMBench: Multiple-implementation of KinectFusion
- FOVIS: RGB-D visual odometry
- GTSAM: General smoothing and mapping library for Robotics and SFM - - Georgia Institute of Technology
- FabMap: appearance-based loop closure system - also available in [OpenCV2.4.11](http://docs.opencv.org/modules/contrib/doc/openfabmap.html)
- DBoW2: binary bag-of-words loop detection system
- RatSLAM
- Geometric Context - Derek Hoiem (CMU)
- Recovering Spatial Layout - Varsha Hedau (UIUC)
- Geometric Reasoning - David C. Lee (CMU)
- INRIA Object Detection and Localization Toolkit
- Discriminatively trained deformable part models
- Histograms of Sparse Codes for Object Detection
- BING: Objectness Estimation
- ReInspect
- ANN: A Library for Approximate Nearest Neighbor Searching
- FLANN - Fast Library for Approximate Nearest Neighbors
- PatchMatch
- Generalized PatchMatch
- Coherency Sensitive Hashing
- TreeCANN
- 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
- NeuralTalk
- 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
- LIBSVM -- A Library for Support Vector Machines
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- Blur-kernel estimation from spectral irregularities
- PlanarStructureCompletion
- Spectral Matting
- Graph-based Segmentation
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- PlanarStructureCompletion
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Ground Truth Stixel Dataset
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- PlanarStructureCompletion
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- C++/MatLab Optical Flow by C. Liu (based on Brox et al. and Bruhn et al.)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Secrets of Optical Flow Estimation and Their Principles
- Transformed Self-Exemplars
- Unnatural L0 Sparse Representation for Natural Image Deblurring
- PlanarStructureCompletion
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- PlanarStructureCompletion
- Transformed Self-Exemplars
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- Blur-kernel estimation from spectral irregularities
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
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Datasets
- Sun dataset
- Visual Tracker Benchmark v1.1
- VOT Challenge
- Princeton Tracking Benchmark
- Middlebury Optical Flow Evaluation
- Are we there yet? - Which paper provides the best results on standard dataset X?
- Yet Another Computer Vision Index To Datasets
- ComputerVisionOnline Datasets
- CVOnline Dataset
- CV datasets
- visionbib
- Middlebury Stereo Vision
- The KITTI Vision Benchmark Suite
- LIBELAS: Library for Efficient Large-scale Stereo Matching
- Ground Truth Stixel Dataset
- The KITTI Vision Benchmark Suite
- 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
- VIRAT
- CAM2
- ChangeDetection.net
- The PASCAL Visual Object Classes
- ImageNet Object Detection Challenge
- SUN Database
- Place Dataset
- 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
- Flickr 8K
- Flickr 30K
- SUN RGB-D - A RGB-D Scene Understanding Benchmark Suite
- NYU depth v2 - Indoor Segmentation and Support Inference from RGBD Images
- Ground Truth Stixel Dataset
- Ground Truth Stixel Dataset
- Ground Truth Stixel Dataset
- Ground Truth Stixel Dataset
- Ground Truth Stixel Dataset
- Ground Truth Stixel Dataset
- Ground Truth Stixel Dataset
- Ground Truth Stixel Dataset
- MPI-Sintel Optical Flow Dataset and Evaluation
- HCI Challenge
- Ground Truth Stixel Dataset
- Sun dataset
- Ground Truth Stixel Dataset
- Visual Tracker Benchmark
- Computer Vision Dataset on the web
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Resources for students
- Writing Research Papers - Aaron Hertzmann (Adobe Research)
- 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)
- 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 Paper for SIGGRAPH - Jim Blinn
- How to Get Your SIGGRAPH Paper Rejected - Jim Kajiya (Microsoft Research)
- 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)
- Time Management - Randy Pausch (CMU)
- Resources for students - Frédo Durand (MIT)
- Notes on writing - Frédo Durand (MIT)
- How to Write a Bad Article - Frédo Durand (MIT)
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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
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Links
-
Songs
Programming Languages
Categories
Sub Categories