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DAGM
2011
Springer
14 years 6 months ago
Agnostic Domain Adaptation
The supervised learning paradigm assumes in general that both training and test data are sampled from the same distribution. When this assumption is violated, we are in the setting...
Alexander Vezhnevets, Joachim M. Buhmann
CVPR
2008
IEEE
16 years 8 months ago
Latent topic random fields: Learning using a taxonomy of labels
An important problem in image labeling concerns learning with images labeled at varying levels of specificity. We propose an approach that can incorporate images with labels drawn...
Xuming He, Richard S. Zemel
ICPR
2010
IEEE
15 years 9 months ago
Efficient Learning to Label Images
Conditional random field methods (CRFs) have gained popularity for image labeling tasks in recent years. In this paper, we describe an alternative discriminative approach, by exte...
Ke Jia, Li Cheng, Nianjun Liu, Lei Wang
ICCV
2003
IEEE
16 years 8 months ago
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories
Learning visual models of object categories notoriously requires thousands of training examples; this is due to the diversity and richness of object appearance which requires mode...
Fei-Fei Li 0002, Robert Fergus, Pietro Perona
ISBI
2008
IEEE
16 years 7 months ago
Template-based multiple hypotheses tracking of small vessels
A template tracking approach to the segmentation of small 3D vessel structures is presented. The main contributions are a general formulation of a vessel template function and a m...
Ola Friman, Milo Hindennach, Heinz-Otto Peitgen