[ICCV 2013 Tutorial : Decision Forests and Fields for Computer
Vision]
(http://research.microsoft.com/en-us/um/cambridge/projects/iccv2013tutorial/)
by Jamie Shotton and Sebastian Nowozin
[Lecture 1]
(http://techtalks.tv/talks/randomized-decision-forests-and-their-applications-in-computer-vision-jamie/59432/)
: Randomized Decision Forests and their Applications in Computer Vision
I (Decision Forest, Classification Forest,
[Lecture 2]
(http://techtalks.tv/talks/decision-jungles-jamie-second-half-of-above/59434/)
: Randomized Decision Forests and their Applications in Computer Vision
II (Regression Forest, Decision Jungle)
[Lecture 3]
(http://techtalks.tv/talks/entropy-estimation-and-streaming-data-sebastian/59433/)
: Entropy estimation and streaming data
[Lecture 4]
(http://techtalks.tv/talks/decision-and-regression-tree-fields-sebastian/59435/)
: Decision and Regression Tree Fields
[UBC Machine Learning]
(http://www.cs.ubc.ca/~nando/540-2013/lectures.html) by Nando de Freitas
Tom Rainforth, and Frank Wood, Canonical Correlation Forests, arxiv
2015
Relating Cascaded Random Forests to Deep Convolutional Neural
Networks [[Paper] (http://arxiv.org/pdf/1507.07583.pdf)]
David L Richmond, Dagmar Kainmueller, Michael Y Yang, Eugene W
Myers, and Carsten Rother, Relating Cascaded Random Forests to Deep
Convolutional Neural Networks for Semantic Segmentation, arxiv 2015
Balaji Lakshminarayanan, Daniel M. Roy and Yee Whye Teh, Mondrian
Forests: Efficient Online Random Forests, NIPS 2014
Extremely randomized trees P Geurts, D Ernst, L Wehenkel - Machine
learning, 2006 [Paper]
[[Code]
(http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html)]
Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John
Winn, and Antonio Criminisi, Decision Jungles: Compact and Rich Models
for Classification, NIPS 2013
Laptev, Dmitry, and Joachim M. Buhmann. Transformation-invariant
convolutional jungles. CVPR 2015. [Paper]
Semi-supervised Node Splitting for Random Forest Construction
[[Paper]
(http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Liu_Semi-supervised_Node_Splitting_2013_CVPR_paper.pdf)]
Xiao Liu, Mingli Song, Dacheng Tao, Zicheng Liu, Luming Zhang, Chun
Chen and Jiajun Bu, Semi-supervised Node Splitting for Random Forest
Construction, CVPR 2013
Improved Information Gain Estimates for Decision Tree Induction
[[Paper]
(http://www.nowozin.net/sebastian/papers/nowozin2012infogain.pdf)]
Sebastian Nowozin, Improved Information Gain Estimates for Decision
Tree Induction, ICML 2012
MIForests: Multiple-Instance Learning with Randomized Trees [[Paper]
(http://lrs.icg.tugraz.at/pubs/leistner_eccv_10.pdf)] [[Code]
(http://www.ymer.org/amir/software/milforests/)]
Christian Leistner, Amir Saffari, and Horst Bischof, MIForests:
Multiple-Instance Learning with Randomized Trees, ECCV 2010
Samuel Schulter, Paul Wohlhart, Christian Leistner, Amir Saffari,
Peter M. Roth, Horst Bischof: Alternating Decision Forests. CVPR 2013 Paper
Decision Forests, Convolutional Networks and the Models in-Between
[Paper]
ETH Zurich [[Paper-CVPR15]
(http://www.iai.uni-bonn.de/~gall/download/jgall_coarse2fine_cvpr15.pdf)]
[[Paper-CVPR14]
(http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Ristin_Incremental_Learning_of_2014_CVPR_paper.pdf)]
[[Paper-ECCV]
(http://www.vision.ee.ethz.ch/~lbossard/bossard_eccv14_food-101.pdf)]
Marko Ristin, Juergen Gall, Matthieu Guillaumin, and Luc Van Gool,
From Categories to Subcategories: Large-scale Image Classification with
Partial Class Label Refinement, CVPR 2015
Marko Ristin, Matthieu Guillaumin, Juergen Gall, and Luc Van Gool,
Incremental Learning of NCM Forests for Large-Scale Image
Classification, CVPR 2014
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool, Food-101 –
Mining Discriminative Components with Random Forests, ECCV 2014
University of Girona & University of Oxford [[Paper]
(http://www.cs.huji.ac.il/~daphna/course/CoursePapers/bosch07a.pdf)]
Anna Bosch, Andrew Zisserman, and Xavier Munoz, Image Classification
using Random Forests and Ferns, ICCV 2007
Object Detection
Graz University of Technology [[Paper-CVPR]
(http://lrs.icg.tugraz.at/pubs/schulter_cvpr_14.pdf)] [[Paper-ICCV]
(http://lrs.icg.tugraz.at/pubs/schulter_iccv_13.pdf)]
Samuel Schulter, Christian Leistner, Paul Wohlhart, Peter M. Roth,
and Horst Bischof, Accurate Object Detection with Joint
Classification-Regression Random Forests, CVPR 2014
Samuel Schulter, Christian Leistner, Paul Wohlhart, Peter M. Roth,
and Horst Bischof, Alternating Regression Forests for Object Detection
and Pose Estimation, ICCV 2013
ETH Zurich + Microsoft Research Cambridge [[Paper]
(http://www.iai.uni-bonn.de/~gall/download/jgall_houghforest_cvpr09.pdf)]
Juergen Gall, and Victor Lempitsky, Class-Specific Hough Forests for
Object Detection, CVPR 2009
David Joseph Tan, and Slobodan Ilic, Multi-Forest Tracker: A
Chameleon in Tracking, CVPR 2014
ETH Zurich + Leibniz University Hannover + Stanford University
[[Paper]
(http://www.igp.ethz.ch/photogrammetry/publications/pdf_folder/LeaFenKuzRosSavCVPR14.pdf)]
Laura Leal-Taixe, Michele Fenzi, Alina Kuznetsova, Bodo Rosenhahn,
and Silvio Savarese, Learning an image-based motion context for multiple
people tracking, CVPR 2014
Graz University of Technology [[Paper]
(https://lrs.icg.tugraz.at/pubs/godec_iccv_11.pdf)]
Martin Godec, Peter M. Roth, and Horst Bischof, Hough-based Tracking
of Non-Rigid Objects, ICCV 2011
Edge Detection
University of California, Irvine [[Paper]
(http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Hallman_Oriented_Edge_Forests_2015_CVPR_paper.pdf)]
[[Code] (https://github.com/samhallman/oef)]
Sam Hallman, and Charless C. Fowlkes, Oriented Edge Forests for
Boundary Detection, CVPR 2015
Microsoft Research [[Paper]
(http://research-srv.microsoft.com/pubs/202540/DollarICCV13edges.pdf)]
[[Code] (https://github.com/pdollar/edges)]
Piotr Dollar, and C. Lawrence Zitnick, Structured Forests for Fast
Edge Detection, ICCV 2013
Massachusetts Inst. of Technology + Microsoft Research [[Paper]
(http://research.microsoft.com/en-us/um/people/larryz/cvpr13sketchtokens.pdf)]
[[Code] (https://github.com/joelimlimit/SketchTokens)]
Joseph J. Lim, C. Lawrence Zitnick, and Piotr Dollar, Sketch Tokens:
A Learned Mid-level Representation for Contour and Object Detection,
CVPR 2013
Semantic Segmentation
Fondazione Bruno Kessler, Microsoft Research Cambridge [[Paper]
(http://www.dsi.unive.it/~srotabul/files/publications/CVPR2014a.pdf)]
Samuel Rota Bulo, and Peter Kontschieder, Neural Decision Forests
for Semantic Image Labelling, CVPR 2014
INRIA + Microsoft Research Cambridge [[Paper]
(http://step.polymtl.ca/~rv101/MICCAI-Laplacian-Forest.pdf)]
Herve Lombaert, Darko Zikic, Antonio Criminisi, and Nicholas Ayache,
Laplacian Forests:Semantic Image Segmentation by Guided Bagging, MICCAI
2014
Microsoft Research Cambridge + GE Global Research Center +
University of California + Rutgers Univeristy [[Paper]
(http://research.microsoft.com/pubs/146430/criminisi_ipmi_2011c.pdf)]
Albert Montillo1, Jamie Shotton, John Winn, Juan Eugenio Iglesias,
Dimitri Metaxas, and Antonio Criminisi, Entangled Decision Forests and
their Application for Semantic Segmentation of CT Images, IPMI 2011
University of Cambridge + Toshiba Corporate R&D Center [[Paper]
(http://mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2008-CVPR-semantic-texton-forests.pdf)]
Jamie Shotton, Matthew Johnson, and Roberto Cipolla, Semantic Texton
Forests for Image Categorization and Segmentation, CVPR 2008
Human / Hand Pose Estimation
Microsoft Research Cambridge [[Paper-CHI]
(http://research.microsoft.com/pubs/238453/pn362-sharp.pdf)][[Video-CHI]
(http://research.microsoft.com/pubs/238453/pn362-sharp-video.mp4)]
[[Paper-CVPR] (http://research.microsoft.com/pubs/162510/vm.pdf)]
Toby Sharp, Cem Keskin, Duncan Robertson, Jonathan Taylor, Jamie
Shotton, David Kim, Christoph Rhemann, Ido Leichter, Alon Vinnikov,
Yichen Wei, Daniel Freedman, Pushmeet Kohli, Eyal Krupka, Andrew
Fitzgibbon, and Shahram Izadi, Accurate, Robust, and Flexible Real-time
Hand Tracking, CHI 2015
Jonathan Taylor, Jamie Shotton, Toby Sharp, and Andrew Fitzgibbon,
The Vitruvian Manifold:Inferring Dense Correspondences for One-Shot
Human Pose Estimation, CVPR 2012
Microsoft Research Haifa [[Paper]
(http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Krupka_Discriminative_Ferns_Ensemble_2014_CVPR_paper.pdf)]
Eyal Krupka, Alon Vinnikov, Ben Klein, Aharon Bar Hillel, and Daniel
Freedman, Discriminative Ferns Ensemble for Hand Pose Recognition, CVPR
2014
Microsoft Research Asia [[Paper]
(http://research.microsoft.com/en-us/people/yichenw/cvpr14_facealignment.pdf)]
Shaoqing Ren, Xudong Cao, Yichen Wei, and Jian Sun, Face Alignment
at 3000 FPS via Regressing Local Binary Features, CVPR 2014
Imperial College London [[Paper-CVPR-Face]
(http://www.iis.ee.ic.ac.uk/icvl/doc/cvpr14_xiaowei.pdf)]
[[Paper-CVPR-Hand]
(http://www.iis.ee.ic.ac.uk/icvl/doc/cvpr14_danny.pdf)] [[Paper-ICCV]
(http://www.iis.ee.ic.ac.uk/icvl/doc/ICCV13_danny.pdf)]
Xiaowei Zhao, Tae-Kyun Kim, and Wenhan Luo, Unified Face Analysis by
Iterative Multi-Output Random Forests, CVPR 2014
Danhang Tang, Hyung Jin Chang, Alykhan Tejani, and Tae-Kyun Kim,
Latent Regression Forest: Structured Estimation of 3D Articulated Hand
Posture, CVPR 2014
Danhang Tang, Tsz-Ho Yu, and Tae-Kyun Kim, Real-time Articulated
Hand Pose Estimation using Semi-supervised Transductive Regression
Forests, ICCV 2013
ETH Zurich + Microsoft [[Paper]
(https://lirias.kuleuven.be/bitstream/123456789/398648/2/3601_open+access.pdf)]
Matthias Dantone, Juergen Gall, Christian Leistner, and Luc Van
Gool, Human Pose Estimation using Body Parts Dependent Joint Regressors,
CVPR 2013
3D localization
Imperial College London [[Paper]
(http://www.iis.ee.ic.ac.uk/icvl/doc/ECCV2014_aly.pdf)]
Alykhan Tejani, Danhang Tang, Rigas Kouskouridas, and Tae-Kyun Kim,
Latent-Class Hough Forests for 3D Object Detection and Pose Estimation,
ECCV 2014
Microsoft Research Cambridge + University of Illinois + Imperial
College London [[Paper]
(http://abnerguzman.com/publications/gkgssfi_cvpr14.pdf)]
Abner Guzman-Rivera, Pushmeet Kohli, Ben Glocker, Jamie Shotton,
Toby Sharp, Andrew Fitzgibbon, and Shahram Izadi, Multi-Output Learning
for Camera Relocalization, CVPR 2014
Microsoft Research Cambridge [[Paper]
(http://research.microsoft.com/pubs/184826/relocforests.pdf)]
Jamie Shotton, Ben Glocker, Christopher Zach, Shahram Izadi, Antonio
Criminisi, and Andrew Fitzgibbon, Scene Coordinate Regression Forests
for Camera Relocalization in RGB-D Images, CVPR 2013
Graz University of Technology [[Paper]
(http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Schulter_Fast_and_Accurate_2015_CVPR_paper.pdf)]
Samuel Schulter, Christian Leistner, and Horst Bischof, Fast and
Accurate Image Upscaling with Super-Resolution Forests, CVPR 2015
Denoising
Microsoft Research + iCub Facility - Istituto Italiano di Tecnologia
[[Paper]
(http://research.microsoft.com/pubs/217099/CVPR2014ForestFiltering.pdf)]
Sean Ryan Fanello, Cem Keskin, Pushmeet Kohli, Shahram Izadi, Jamie
Shotton, Antonio Criminisi, Ugo Pattacini, and Tim Paek, Filter Forests
for Learning Data-Dependent Convolutional Kernels, CVPR 2014
Arnaud Dapogny, Kevin Bailly, and Severine Dubuisson, Pairwise
Conditional Random Forests for Facial Expression Recognition, ICCV
2015
Interpretability,
regularization, compression pruning and feature selection
Global Refinement of Random Forest [[Paper]
(http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Ren_Global_Refinement_of_2015_CVPR_paper.pdf)]
Shaoqing Ren, Xudong Cao, Yichen Wei, Jian Sun, Global Refinement of
Random Forest, CVPR 2015
L1-based compression of random forest models Arnaud Joly, Fran¸cois
Schnitzler, Pierre Geurts and Louis Wehenkel ESANN 2012 [Paper]
Feature-Budgeted Random Forest [[Paper]
(http://jmlr.org/proceedings/papers/v37/nan15.pdf)] [Supp]
Feng Nan, Joseph Wang, Venkatesh Saligrama, Feature-Budgeted Random
Forest, ICML 2015
Pruning Random Forests for Prediction on a Budget Feng Nan, Joseph
Wang, Venkatesh Saligrama NIPS 2016 [Paper]
Meinshausen, Nicolai. “Node harvest.” The Annals of Applied
Statistics 4.4 (2010): 2049-2072. [Paper]
[Code
R] [Code
Python]
Making Tree Ensembles Interpretable: A Bayesian Model Selection
Approach S. Hara, K. Hayashi, [Paper] [Code]
Cui, Zhicheng, et al. “Optimal action extraction for random forests
and boosted trees.” ACM SIGKDD 2015. [Paper]
DART: Dropouts meet Multiple Additive Regression Trees K. V. Rashmi,
Ran Gilad-Bachrach [Paper]
Begon, Jean-Michel, Arnaud Joly, and Pierre Geurts. Joint learning
and pruning of decision forests. (2016). [Paper]
