This talk presents recent work from CVPR that looks at inference for pairwise CRF models in the highly (or fully) connected case rather than simply a sparse set of neighbours used ubiquitously in many computer vision tasks. Recent work has shown that fully-connected CRFs, where each node is connected to every other node, can be solved very efficiently under the restriction that the pairwise term is a Gaussian kernel over a Euclidean feature space. The method presented generalises this model to allow arbitrary, non-parametric models (which can be learnt from training data and conditioned on test data) to be used for the pairwise potentials. This greatly increases the expressive power of such models whilst maintaining efficient inference.
Biography: Neill completed his PhD at the University of Cambridge with Roberto Cipolla and at Toshiba Research, advised by Carlos Hernandez and George Vogiatzis, developing CRF (Conditional Random Field) models for multi-view object segmentation and stereo. He then moved to University College London and is now a Post-Doc with Jan Kautz and Simon Prince where he divides his time between computer graphics, machine learning and computer vision, currently working on synthesising photo-realistic objects, active learning and CRF inference.