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CVPR
2009
IEEE

Co-training with Noisy Perceptual Observations

14 years 10 months ago
Co-training with Noisy Perceptual Observations
Many perception and multimedia indexing problems involve datasets that are naturally comprised of multiple streams or modalities for which supervised training data is only sparsely available. In cases where there is a degree of conditional independence between such views, a class of semi-supervised techniques based on maximizing view agreement over unlabeled data has been proven successful in a wide range of machine learning domains. However, these ‘co-training’ or ‘multi-view’ learning methods have had relatively limited application in vision, due in part to the assumption of constant per-channel noise models. In this paper we propose a probabilistic heteroscedastic approach to co-training that simultaneously discovers the amount of noise in a per example basis, while solving the classification task. This results in high performance in the presence of occlusion or other complex observation noise processes. We demonstrate our approach in two domains, multi-view object recogniti...
Ashish Kapoor, Chris Mario Christoudias, Raquel Ur
Added 09 May 2009
Updated 10 Dec 2009
Type Conference
Year 2009
Where CVPR
Authors Ashish Kapoor, Chris Mario Christoudias, Raquel Urtasun, Trevor Darrell
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