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» Learning Generative Models via Discriminative Approaches
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CVPR
2009
IEEE
1119views Computer Vision» more  CVPR 2009»
15 years 6 months ago
Adaptive Contour Features in Oriented Granular Space for Human Detection and Segmentation
In this paper, a novel feature named Adaptive Contour Feature (ACF) is proposed for human detection and segmentation. This feature consists of a chain of a number of granules in...
Wei Gao (Tsinghua University), Haizhou Ai (Tsinghu...
ISVC
2009
Springer
14 years 5 months ago
Weight, Sex, and Facial Expressions: On the Manipulation of Attributes in Generative 3D Face Models
Generative 3D Face Models are expressive models with applications in modelling and editing. They are learned from example faces, and offer a compact representation of the continuou...
Brian Amberg, Pascal Paysan, Thomas Vetter
ECCV
2008
Springer
15 years 1 months ago
Multiple Component Learning for Object Detection
Abstract. Object detection is one of the key problems in computer vision. In the last decade, discriminative learning approaches have proven effective in detecting rigid objects, a...
Boris Babenko, Pietro Perona, Piotr Dollár,...
ICML
1999
IEEE
15 years 5 hour ago
Approximation Via Value Unification
: Numerical function approximation over a Boolean domain is a classical problem with wide application to data modeling tasks and various forms of learning. A great many function ap...
Paul E. Utgoff, David J. Stracuzzi
GECCO
2005
Springer
232views Optimization» more  GECCO 2005»
14 years 4 months ago
Factorial representations to generate arbitrary search distributions
A powerful approach to search is to try to learn a distribution of good solutions (in particular of the dependencies between their variables) and use this distribution as a basis ...
Marc Toussaint