John M. Winn

发表

Entangled Decision Forests and Their Application for Semantic Segmentation of CT Images

Dimitris N. Metaxas, Antonio Criminisi, Albert Montillo, 2011, IPMI.

Learning Direct Optimization for Scene Understanding

Christopher K. I. Williams, John M. Winn, Lukasz Romaszko, 2020, Pattern Recognit..

Discriminative Object Class Models of Appearance and Shape by Correlatons

Silvio Savarese, Antonio Criminisi, John M. Winn, 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs pdf

Nicolas Le Roux, Nicolas Heess, John M. Winn, 2011, ICANN.

Located Hidden Random Fields: Learning Discriminative Parts for Object Detection

Ashish Kapoor, John M. Winn, Ashish Kapoor, 2006, ECCV.

Just-In-Time Learning for Fast and Flexible Inference

Pushmeet Kohli, Daniel Tarlow, John M. Winn, 2014, NIPS.

Escaping local minima through hierarchical model selection: Automatic object discovery, segmentation, and tracking in video

Nebojsa Jojic, C. Lawrence Zitnick, John M. Winn, 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

Epitomic Location Recognition

Antonio Criminisi, Kai Ni, John M. Winn, 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Causality with Gates

John M. Winn, J. Winn, 2012, AISTATS.

TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation

Antonio Criminisi, Carsten Rother, Jamie Shotton, 2006, ECCV.

Learning to Pass Expectation Propagation Messages

Daniel Tarlow, Nicolas Heess, John M. Winn, 2013, NIPS.

Structured Variational Distributions in VIBES

Christopher M. Bishop, John M. Winn, Charles M. Bishop, 2003, AISTATS.

The Pascal Visual Object Classes Challenge: A Retrospective

Luc Van Gool, Andrew Zisserman, Christopher K. I. Williams, 2014, International Journal of Computer Vision.

Clustering appearance and shape by learning jigsaws

Carsten Rother, John M. Winn, Anitha Kannan, 2006, NIPS.

Photo clip art

Alexei A. Efros, Antonio Criminisi, Derek Hoiem, 2007, ACM Trans. Graph..

Hybrid learning of large jigsaws

John M. Winn, Anitha Kannan, Julia A. Lasserre, 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

Non-linear Bayesian Image Modelling

Christopher M. Bishop, John M. Winn, Charles M. Bishop, 2000, ECCV.

TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context

Antonio Criminisi, Carsten Rother, Jamie Shotton, 2007, International Journal of Computer Vision.

Generative Affine Localisation and Tracking

Andrew Blake, John M. Winn, J. Winn, 2004, NIPS.

Consensus Message Passing for Layered Graphical Models pdf

Varun Jampani, Pushmeet Kohli, Daniel Tarlow, 2014, AISTATS.

VIBES: A Variational Inference Engine for Bayesian Networks

David J. Spiegelhalter, Christopher M. Bishop, John M. Winn, 2002, NIPS.

Bilayer Segmentation of Webcam Videos Using Tree-Based Classifiers

Irfan A. Essa, Antonio Criminisi, Pei Yin, 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables

Nevena Lazic, Christopher M. Bishop, John M. Winn, 2013, AISTATS.

Tree-based Classifiers for Bilayer Video Segmentation

Irfan A. Essa, Antonio Criminisi, Pei Yin, 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

LOCUS: learning object classes with unsupervised segmentation

Nebojsa Jojic, John M. Winn, J. Winn, 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

A unified modeling approach to data-intensive healthcare

Iain E. Buchan, Christopher M. Bishop, John M. Winn, 2009, The Fourth Paradigm.

Learning a Generative Model of Images by Factoring Appearance and Shape

Nicolas Le Roux, Nicolas Heess, Jamie Shotton, 2011, Neural Computation.

The Shape Boltzmann Machine: A strong model of object shape

Nicolas Heess, John M. Winn, S. M. Ali Eslami, 2012, CVPR.

The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects

Jamie Shotton, John M. Winn, J. Winn, 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

Accounting for Non-genetic Factors Improves the Power of eQTL Studies

Oliver Stegle, John M. Winn, Richard Durbin, 2008, RECOMB.

The Pascal Visual Object Classes (VOC) Challenge

Luc Van Gool, Andrew Zisserman, Christopher K. I. Williams, 2010, International Journal of Computer Vision.

A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies

Leopold Parts, Oliver Stegle, John M. Winn, 2010, PLoS Comput. Biol..

Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering pdf

Ryota Tomioka, John M. Winn, Liwen Zhang, 2016, ArXiv.

The Shape Boltzmann Machine: A Strong Model of Object Shape

Christopher K. I. Williams, Nicolas Heess, John M. Winn, 2012, International Journal of Computer Vision.

Incorporating On-demand Stereo for Real Time Recognition

Ankur Agarwal, Thomas Deselaers, Antonio Criminisi, 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

In Memoriam: Mark Everingham

Andrew W. Fitzgibbon, Luc Van Gool, Andrew Zisserman, 2012, IEEE Trans. Pattern Anal. Mach. Intell..

3D LayoutCRF for Multi-View Object Class Recognition and Segmentation

Derek Hoiem, Carsten Rother, John M. Winn, 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

Bayesian association of haplotypes and non-genetic factors to regulatory and phenotypic variation in human populations

John M. Winn, Anitha Kannan, Jim C. Huang, 2007, ISMB/ECCB.

Object categorization by learned universal visual dictionary

Antonio Criminisi, Tom Minka, John M. Winn, 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

Immune System Modeling with Infer.NET

Vincent Y. F. Tan, John M. Winn, Adnan Custovic, 2008, 2008 IEEE Fourth International Conference on eScience.

Variational Message Passing and its Applications

John M. Winn, J. Winn, 2004 .

Decision Jungles: Compact and Rich Models for Classification

Sebastian Nowozin, Toby Sharp, Antonio Criminisi, 2013, NIPS.

ShapePheno: unsupervised extraction of shape phenotypes from biological image collections

Daniel P. Miranker, Oliver Stegle, Karsten M. Borgwardt, 2012, Bioinform..