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- One-shot_learning abstract "One-shot learning is an object categorization problem of current research interest in computer vision. Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of images and very large datasets, one-shot learning aims to learn information about object categories from one, or only a few, training images. The primary focus of this article will be on the solution to this problem presented by L. Fei-Fei, R. Fergus and P. Perona in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol28(4), 2006, which uses a generative object category model and variational Bayesian framework for representation and learning of visual object categories from a handful of training examples. Another paper, presented at the International Conference on Computer Vision and Pattern Recognition (CVPR) 2000 by Erik Miller, Nicholas Matsakis, and Paul Viola will also be discussed.".
- One-shot_learning wikiPageExternalLink Fei-Fei_ICDL2006.pdf.
- One-shot_learning wikiPageExternalLink Fei-FeiFergusPerona2006.pdf.
- One-shot_learning wikiPageExternalLink cvpr2000.pdf.
- One-shot_learning wikiPageID "15261672".
- One-shot_learning wikiPageRevisionID "586186869".
- One-shot_learning hasPhotoCollection One-shot_learning.
- One-shot_learning id "attias".
- One-shot_learning id "bart".
- One-shot_learning id "biederman".
- One-shot_learning id "burl1996".
- One-shot_learning id "fegus2005".
- One-shot_learning id "feifeipami2006".
- One-shot_learning id "fink".
- One-shot_learning id "hoeim".
- One-shot_learning id "idFeiFei2002".
- One-shot_learning id "idFeiFeiICDL2006".
- One-shot_learning id "kadir2001".
- One-shot_learning id "miller2000".
- One-shot_learning id "murphy".
- One-shot_learning id "thorpe1996".
- One-shot_learning id "weber2000".
- One-shot_learning reference "Bart and Ullman "Cross-generalization: learning novel classes from a single example by feature replacement". CVPR, 2005.".
- One-shot_learning reference "D. Hoiem, A.A. Efros, and M. Herbert, "Geometric context from a single image". ICCV, 2005.".
- One-shot_learning reference "F.F. Li, R. VanRullen, C.Coch, and P. Perona, "Rapid natural scene categorization in the near absence of attention". PNAS, 99:9596-9601, 2002.".
- One-shot_learning reference "H. Attias, "Inferring Parameters and Structure of Latent Variable Models by Variational Bayes". Proc. of the 15th Conf. in Uncertainty in Artificial Intelligence, pp. 21-30, 1999.".
- One-shot_learning reference "I. Biederman. "Recognition-by-Components: a theory of human understanding". Psychological Review, 94:115-147, 1987.".
- One-shot_learning reference "K. Murphy, A. Torralba, W.T. Freeman, "Using the forest to see the trees: a graphical model relating features, objects, and scenes". NIPS, 2004.".
- One-shot_learning reference "L. Fei-Fei, "Knowledge transfer in learning to recognize visual object classes." International Conference on Development and Learning . 2006. PDF".
- One-shot_learning reference "L. Fei-Fei, R. Fergus and P. Perona, "One-Shot learning of object categories". IEEE Trans. Pattern Analysis and Machine Intelligence, Vol28, 594 - 611, 2006.PDF".
- One-shot_learning reference "M. Burl, M. Weber, and P. Perona, "A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry". Proc. European Conf. Computer Vision, pp. 628-641, 1996.".
- One-shot_learning reference "M. Fink, "Object classification from a single example utilizing class relevance pseudo-metrics". NIPS, 2004.".
- One-shot_learning reference "M. Weber, M. Welling, and P. Perona, "Unsupervised Learning of Models for Recognition". Proc. European Conf. Computer Vision, pp. 101-108, 2000.".
- One-shot_learning reference "Miller, Matsakis, and Viola, "Learning from One Example through Shared Densities on Transforms". Proc. Computer Vision and Pattern Recognition, 2000.PDF".
- One-shot_learning reference "R. Fergus, P. Perona, and A. Zisserman, "Object Class Recognition by Unsupervised Scale-Invariant Learning". Proc. Computer Vision and Pattern Recognition, pp. 264-271, 2003.".
- One-shot_learning reference "S. Thorpe, D. Fize, and C. Marlot, "Speed of processing in the human visual system". Nature, 381:520-522, 1996.".
- One-shot_learning reference "T. Kadir and M. Brady, "Scale, Saliency, and Image Description". Int'l J. of Computer Vision, vol. 45, no. 2, pp. 83-105, 2001.".
- One-shot_learning subject Category:Learning_in_computer_vision.
- One-shot_learning comment "One-shot learning is an object categorization problem of current research interest in computer vision. Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of images and very large datasets, one-shot learning aims to learn information about object categories from one, or only a few, training images. The primary focus of this article will be on the solution to this problem presented by L. Fei-Fei, R. Fergus and P.".
- One-shot_learning label "One-shot learning".
- One-shot_learning sameAs m.03hnhvz.
- One-shot_learning sameAs Q7092335.
- One-shot_learning sameAs Q7092335.
- One-shot_learning wasDerivedFrom One-shot_learning?oldid=586186869.
- One-shot_learning isPrimaryTopicOf One-shot_learning.