I am the head of the Computer Vision and Learning group at the Idiap research institute in Switzerland, and I am member of the École Polytechnique Fédérale de Lausanne faculty as Maître d'Enseignement et de Recherche. I received the PhD degree in Mathematics from University of Paris VI in 2000, and the habilitation degree in Applied Mathematics from University of Paris XIII in 2006.
My research is about algorithmic for machine learning and probabilistic models, mostly for computer vision. I am looking at how to use large feature spaces efficiently for object detection with Charles Dubout, at adapting Boosting to an on-line setting with Leonidas Lefakis, and at improving unsupervised feature learning with Olivier Canévet. With Riwal Lefort, and groups from Basel, Geneva and EPFL, we are investigating the prediction of phenotypical properties of neurons from gene expression.
I am the coordinator of the European project MASH. Our objective is to design an open platform to build a complex machine-learning system. Anyone can submit image feature extractors, and they are automatically combined with machine learning algorithms to solve problems in computer vision and action selection. You can check a presentation of the project at the ML Summer School @Purdue, 2011, or give it a try on the fully on-line MASH Factory.
My main ongoing collaborations are with the CVLab at the EPFL, the Vision Lab at Caltech, and the Institut de Robòtica i Informàtica Industrial at the Technical University of Catalonia.
My research is supported by the Swiss National Science Foundation, the 7th Research Framework Program of the European Commission, and the Hasler Foundation.
R. Sznitman, C. Becker, F. Fleuret, and P. Fua. Fast Object Detection with Entropy-Driven Evaluation. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), 2013. To appear. bib · pdf
C. Dubout and F. Fleuret. Exact Acceleration of Linear Object Detectors. In Proceedings of the European Conference on Computer Vision (ECCV), pages 301-311, 2012. bib · pdf
F. Fleuret, T. Li, C. Dubout, E. K. Wampler, S. Yantis, and D. Geman. Comparing machines and humans on a visual categorization test. Proceedings of the National Academy of Sciences (PNAS), 108(43):17621-17625, 2011. bib · pdf
C. Dubout and F. Fleuret. Boosting with Maximum Adaptive Sampling. In Proceedings of the international conference on Neural Information Processing Systems (NIPS), pages 1332-1340, 2011. bib · pdf
K. Ali, F. Fleuret, D. Hasler, and P. Fua. A Real-Time Deformable Detector. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 34(2):225-239, 2012. bib · pdf
J. Berclaz, F. Fleuret, E. Turetken, and P. Fua. Multiple Object Tracking using K-Shortest Paths Optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 33(9):1806-1819, 2011. bib · pdf
L. Lefakis and F. Fleuret. Joint Cascade Optimization Using A Product Of Boosted Classifiers. In Proceedings of the international conference on Neural Information Processing Systems (NIPS), pages 1315-1323, 2010. bib · pdf
F. Fleuret and D. Geman. Stationary Features and Cat Detection. Journal of Machine Learning Research (JMLR), 9:2549-2578, 2008. bib · pdf
