My main research interest is in developing optimization and machine learning techniques suitable for solving high-level computer vision tasks, such as image classification and object recognition.
High-level computer vision tasks are a unique source of hard machine learning problems for three reasons. First, in contrast to physics-based processes we do not know the correct model (model uncertainty). Second, humans excel at all high-level vision tasks and therefore can provide data and assess model performance (ground truth oracle). Third, image and video data is available for free at an enormous scale (data availability). These properties make computer vision a particularly attractive area for machine learning research.
I am particularly interested in using mathematical optimization as a tool to solve computer vision machine learning tasks.
My personal homepage is here: http://www.nowozin.net/sebastian/
- Jeremy Jancsary, Sebastian Nowozin, and Carsten Rother, Learning Convex QP Relaxations for Structured Prediction, in 30th International Conference on Machine Learning (ICML), June 2013
- Uwe Schmidt, Carsten Rother, Sebastian Nowozin, Jeremy Jancsary, and Stefan Roth, Discriminative Non-blind Deblurring, in 2013 Conference on Computer Vision and Pattern Recognition (CVPR 2013), IEEE Computer Society, 20 April 2013
- Jeremy Jancsary, Sebastian Nowozin, and Carsten Rother, Non-parametric CRFs for Image Labeling, in NIPS Workshop on Modern Nonparametric Methods in Machine Learning, December 2012
- Jeremy Jancsary, Sebastian Nowozin, and Carsten Rother, Loss-Specific Training of Non-Parametric Image Restoration Models: A New State of the Art, in 12th European Conference on Computer Vision, Springer, 2 August 2012
- Sebastian Nowozin and Jamie Shotton, Action Points: A Representation for Low-latency Online Human Action Recognition, no. MSR-TR-2012-68, 9 July 2012
- Sebastian Nowozin, Improved Information Gain Estimates for Decision Tree Induction, in ICML 2012, , 26 June 2012
- Jeremy Jancsary, Sebastian Nowozin, Toby Sharp, and Carsten Rother, Regression Tree Fields - An Efficient, Non-parametric Approach to Image Labeling Problems, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, 10 April 2012
- Simon Fothergill, Helena M. Mentis, Sebastian Nowozin, and Pushmeet Kohli, Instructing People for Training Gestural Interactive Systems, ACM Conference on Computer-Human Interaction, 2012
- Sebastian Nowozin, Carsten Rother, Shai Bagon, Toby Sharp, Bangpeng Yao, and Pushmeet Kohli, Decision Tree Fields: An Efficient Non-Parametric Random Field Model for Image Labelling, in Decision Forests: for Computer Vision and Medical Image Analysis; Springer: Advances in Computer Vision and Pattern Recognition, Springer, 2012
- Sebastian Nowozin, Carsten Rother, Shai Bagon, Toby Sharp, Bangpeng Yao, and Pushmeet Kohli, Decision Tree Fields, in ICCV, 2011