2009

• Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John Winn, and Andrew Zisserman, The Pascal Visual Object Classes (VOC) Challenge, in International Journal of Computer Vision, Springer Verlag, 9 September 2009
• Tom Minka and John Winn, Gates, in Advances in Neural Information Processing Systems 21, 2009
• Jamie Shotton, John Winn, Carsten Rother, and Antonio Criminisi, Textonboost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Appearance, Shape and Context., in Intl. Journal on Computer Vision (IJCV), special issue., Springer Verlag, 2009
• Kai Ni, Anitha Kannan, Antonio Criminisi, and John Winn, Epitomic Location Recognition, in IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI special issue), IEEE, 2009

2008

• Tom Minka and John Winn, Gates: A graphical notation for mixture models, no. MSR-TR-2008-185, 5 December 2008
• Vincent Y. F. Tan, John Winn, Angela Simpson, and Adnan Custovic, Immune System Modeling with Infer.NET, in IEEE International Conference on e-Science (e-Science 2008), , Indianapolis, Indiana, 1 December 2008
• Oliver Stegle, Anitha Kannan, Richard Durbin, and John M. Winn, Accounting for Non-genetic Factors Improves the Power of eQTL Studies, in International Conference on Research in Computational Molecular Biology, 2008
• Kai Ni, Anitha Kannan, Antonio Criminisi, and John Winn, Epitomic Location Recognition, in Proc IEEE Conference on Computer Vision (CVPR). Winner of BEST STUDENT PAPER RUNNER UP AWARD., IEEE Computer Society, 2008

2007

• Julia Lasserre, Anitha Kannan, and John Winn, Hybrid learning of large jigsaws, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007
• D. Hoeim, C. Rother, and J. Winn, 3D LayoutCRF for Multi-View Object Class Recognition and Segmentation , in Proc. IEEE Computer Vision and Pattern Recognition (CVPR) , Minneapolis, US, 2007
• Pei Yin, Antonio Criminisi, Irfan Essa, and John Winn, Tree-based Classifiers for Bilayer Video Segmentation, in Proc. Conf. Computer Vision and Pattern Recognition, 2007
• Jean Francois Lalonde, Derek Hoiem, Alyosha A Efros, John Winn, Carsten Rother, and Antonio Criminisi, Photo Clip Art, in Proc. ACM SIGGRAPH, 2007
• Thomas Deselaers, Antonio Criminisi, John Winn, and Ankur Agarwal, Incorporating On-demand Stereo for Real Time Recognition, in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2007
• Jim C. Huang, Anitha Kannan, and John M. Winn, Bayesian association of haplotypes and non-genetic factors to regulatory and phenotypic variation in human populations, in International Conference on Intelligent Systems for Molecular Biology, 2007
• Anitha Kannan, John Winn, and Carsten Rother, Clustering appearance and shape by learning jigsaws, in Advances in Neural Information Processing Systems, MIT Press, 2007
• Shahram Izadi, Ankur Agarwal, Antonio Criminisi, John Winn, Andrew Blake, and Andrew Fitzgibbon, C-Slate: Exploring Remote Collaboration on Horizontal Multi-touch Surfaces. , in Proc. IEEE Tabletop, 2007

2006

• John Winn and Antonio Criminisi, Object Class Recognition at a Glance, in Proc. Conf. Computer Vision and Pattern Recognition (CVPR) -- Video Track, 2006
• Jamie Shotton, John Winn, Carsten Rother, and Antonio Criminisi, TextonBoost: Joint Appearance, Shape and Context Modeling for Mulit-Class Object Recognition and Segmentation, in European Conference on Computer Vision (ECCV), January 2006
• Anitha Kannan, John Winn, and Carsten Rother, Clustering appearance and shape by learning jigsaws, in NIPS, 2006
• John Winn and Jamie Shotton, The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects, in Proceedings of IEEE CVPR, January 2006
• Silvio Savarese, Antonio Criminisi, and John Winn, Discriminative Object Class Models of Appearance and Shape by Correlatons, in Proc. IEEE Computer Vision and Pattern Recognition (CVPR)., January 2006
• Nebojsa Jojic, John Winn, and Larry Zitnick, Escaping Local Minima through Hierarchical Model Selection: Automatic Object Discovery, Segmentation, and Tracking in Video, in Proceedings of IEEE CVPR, January 2006
• Ashish Kapoor and John Winn, Located Hidden Random Fields Learning Discriminative Parts for Object Detection, in European Conference on Computer Vision, January 2006

2005

• John Winn and Nebojsa Jojic, LOCUS: Learning Object Classes with Unsupervised Segmentation, in Proc. IEEE Intl. Conf. on Computer Vision (ICCV), January 2005
• John Winn, Antonio Criminisi, and Thomas Minka, Object Categorization by Learned Universal Visual Dictionary, in Proc. IEEE Intl. Conf. on Computer Vision (ICCV)., January 2005

2004

• John Winn and Christopher M. Bishop, Variational Message Passing, in Journal of Machine Learning Research, vol. 5, January 2004
• John Winn and Andrew Blake, Generative Affine Localisation and Tracking, in Advances in Neural Information Processing Systems, January 2004

2003

• C. M. Bishop and J. M. Winn, Structured variational distributions in VIBES, in Proceedings Artificial Intelligence and Statistics, Society for Artificial Intelligence and Statistics, January 2003

2002

• C. M. Bishop, J. M. Winn, and D. Spiegelhalter, VIBES: A variational inference engine for Bayesian networks, in Advances in Neural Information Processing Systems, January 2002

2000

• C. M. Bishop and J. M. Winn, Non-linear Bayesian image modelling, in Proceedings Sixth European Conference on Computer Vision, Springer-Verlag, January 2000