John M. Winn

Publication Activity (10 Years)

Years Active: 2000-2021
Publications (10 Years): 5

Top Topics

Direct Optimization

Scene Understanding

Visual Object Classes

Top Venues

Publications

John M. Winn, Matteo Venanzi, Tom Minka, Ivan Korostelev, John Guiver, Elena Pochernina, Pavel Mishkov, Alex Spengler, Denise J. Wilkins, Siân E. Lindley, Richard Banks, Sam Webster, Yordan Zaykov
Enterprise Alexandria: Online High-Precision Enterprise Knowledge Base Construction with Typed Entities. AKBC (2021)
Lukasz Romaszko, Christopher K. I. Williams, John M. Winn
Learning Direct Optimization for scene understanding. Pattern Recognit. 105 (2020)
John M. Winn, John Guiver, Sam Webster, Yordan Zaykov, Martin Kukla, Dany Fabian
Alexandria: Unsupervised High-Precision Knowledge Base Construction using a Probabilistic Program. AKBC (2019)
Lukasz Romaszko, Christopher K. I. Williams, John M. Winn
Learning Direct Optimization for Scene Understanding. CoRR (2018)
Liwen Zhang, John M. Winn, Ryota Tomioka
Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering. CoRR (2016)
Varun Jampani, S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John M. Winn
Consensus Message Passing for Layered Graphical Models. AISTATS (2015)
Mark Everingham, S. M. Ali Eslami, Luc Van Gool, Christopher K. I. Williams, John M. Winn, Andrew Zisserman
The Pascal Visual Object Classes Challenge: A Retrospective. Int. J. Comput. Vis. 111 (1) (2015)
S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John M. Winn
Just-In-Time Learning for Fast and Flexible Inference. NIPS (2014)
S. M. Ali Eslami, Nicolas Heess, Christopher K. I. Williams, John M. Winn
The Shape Boltzmann Machine: A Strong Model of Object Shape. Int. J. Comput. Vis. 107 (2) (2014)
Varun Jampani, S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John M. Winn
Consensus Message Passing for Layered Graphical Models. CoRR (2014)
Nicolas Heess, Daniel Tarlow, John M. Winn
Learning to Pass Expectation Propagation Messages. NIPS (2013)
Nevena Lazic, Christopher M. Bishop, John M. Winn
Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables. AISTATS (2013)
Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John M. Winn, Antonio Criminisi
Decision Jungles: Compact and Rich Models for Classification. NIPS (2013)
S. M. Ali Eslami, Nicolas Heess, John M. Winn
The Shape Boltzmann Machine: A strong model of object shape. CVPR (2012)
John M. Winn
Causality with Gates. AISTATS (2012)
Theofanis Karaletsos, Oliver Stegle, Christine Dreyer, John M. Winn, Karsten M. Borgwardt
ShapePheno: unsupervised extraction of shape phenotypes from biological image collections. Bioinform. 28 (7) (2012)
Andrew Zisserman, John M. Winn, Andrew W. Fitzgibbon, Luc Van Gool, Josef Sivic, Christopher K. I. Williams, David C. Hogg
In Memoriam: Mark Everingham. IEEE Trans. Pattern Anal. Mach. Intell. 34 (11) (2012)
Nicolas Le Roux, Nicolas Heess, Jamie Shotton, John M. Winn
Learning a Generative Model of Images by Factoring Appearance and Shape. Neural Comput. 23 (3) (2011)
Nicolas Heess, Nicolas Le Roux, John M. Winn
Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs. ICANN (2) (2011)
Pei Yin, Antonio Criminisi, John M. Winn, Irfan A. Essa
Bilayer Segmentation of Webcam Videos Using Tree-Based Classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 33 (1) (2011)
Albert Montillo, Jamie Shotton, John M. Winn, Juan Eugenio Iglesias, Dimitris N. Metaxas, Antonio Criminisi
Entangled Decision Forests and Their Application for Semantic Segmentation of CT Images. IPMI (2011)
Nicolas Heess, Nicolas Le Roux, John M. Winn
Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs CoRR (2011)
Oliver Stegle, Leopold Parts, Richard Durbin, John M. Winn
A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies. PLoS Comput. Biol. 6 (5) (2010)
Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John M. Winn, Andrew Zisserman
The Pascal Visual Object Classes (VOC) Challenge. Int. J. Comput. Vis. 88 (2) (2010)
Jamie Shotton, John M. Winn, Carsten Rother, Antonio Criminisi
TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context. Int. J. Comput. Vis. 81 (1) (2009)
Kai Ni, Anitha Kannan, Antonio Criminisi, John M. Winn
Epitomic Location Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31 (12) (2009)
Oliver Stegle, Anitha Kannan, Richard Durbin, John M. Winn
Accounting for Non-genetic Factors Improves the Power of eQTL Studies. RECOMB (2008)
Tom Minka, John M. Winn
Gates. NIPS (2008)
Kai Ni, Anitha Kannan, Antonio Criminisi, John M. Winn
Epitomic location recognition. CVPR (2008)
Vincent Y. F. Tan, John M. Winn, Angela Simpson, Adnan Custovic
Immune System Modeling with Infer.NET. eScience (2008)
Pei Yin, Antonio Criminisi, John M. Winn, Irfan A. Essa
Tree-based Classifiers for Bilayer Video Segmentation. CVPR (2007)
Thomas Deselaers, Antonio Criminisi, John M. Winn, Ankur Agarwal
Incorporating On-demand Stereo for Real Time Recognition. CVPR (2007)
Jean-François Lalonde, Derek Hoiem, Alexei A. Efros, Carsten Rother, John M. Winn, Antonio Criminisi
Photo clip art. ACM Trans. Graph. 26 (3) (2007)
Shahram Izadi, Ankur Agarwal, Antonio Criminisi, John M. Winn, Andrew Blake, Andrew W. Fitzgibbon
C-Slate: A Multi-Touch and Object Recognition System for Remote Collaboration using Horizontal Surfaces. Tabletop (2007)
Derek Hoiem, Carsten Rother, John M. Winn
3D LayoutCRF for Multi-View Object Class Recognition and Segmentation. CVPR (2007)
Jim C. Huang, Anitha Kannan, John M. Winn
Bayesian association of haplotypes and non-genetic factors to regulatory and phenotypic variation in human populations. ISMB/ECCB (Supplement of Bioinformatics) (2007)
Julia A. Lasserre, Anitha Kannan, John M. Winn
Hybrid learning of large jigsaws. CVPR (2007)
John M. Winn, Jamie Shotton
The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects. CVPR (1) (2006)
Silvio Savarese, John M. Winn, Antonio Criminisi
Discriminative Object Class Models of Appearance and Shape by Correlatons. CVPR (2) (2006)
Nebojsa Jojic, John M. Winn, C. Lawrence Zitnick
Escaping local minima through hierarchical model selection: Automatic object discovery, segmentation, and tracking in video. CVPR (1) (2006)
Jamie Shotton, John M. Winn, Carsten Rother, Antonio Criminisi
: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation. ECCV (1) (2006)
Anitha Kannan, John M. Winn, Carsten Rother
Clustering appearance and shape by learning jigsaws. NIPS (2006)
Ashish Kapoor, John M. Winn
Located Hidden Random Fields: Learning Discriminative Parts for Object Detection. ECCV (3) (2006)
John M. Winn, Antonio Criminisi, Thomas P. Minka
Object Categorization by Learned Universal Visual Dictionary. ICCV (2005)
John M. Winn, Christopher M. Bishop
Variational Message Passing. J. Mach. Learn. Res. 6 (2005)
John M. Winn, Nebojsa Jojic
LOCUS: Learning Object Classes with Unsupervised Segmentation. ICCV (2005)
John M. Winn, Andrew Blake
Generative Affine Localisation and Tracking. NIPS (2004)
Christopher M. Bishop, John M. Winn
Structured Variational Distributions in VIBES. AISTATS (2003)
Christopher M. Bishop, David J. Spiegelhalter, John M. Winn
VIBES: A Variational Inference Engine for Bayesian Networks. NIPS (2002)
Christopher M. Bishop, John M. Winn
Non-linear Bayesian Image Modelling. ECCV (1) (2000)