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IJCV
2007
196views more  IJCV 2007»
13 years 8 months ago
Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition
We investigate a method for learning object categories in a weakly supervised manner. Given a set of images known to contain the target category from a similar viewpoint, learning...
Robert Fergus, Pietro Perona, Andrew Zisserman
ECCV
2008
Springer
14 years 10 months ago
Learning Optical Flow
Assumptions of brightness constancy and spatial smoothness underlie most optical flow estimation methods. In contrast to standard heuristic formulations, we learn a statistical mod...
Deqing Sun, Stefan Roth, J. P. Lewis, Michael J. B...
ICCV
2011
IEEE
12 years 8 months ago
Perturb-and-MAP Random Fields: Using Discrete Optimization\\to Learn and Sample from Energy Models
We propose a novel way to induce a random field from an energy function on discrete labels. It amounts to locally injecting noise to the energy potentials, followed by finding t...
George Papandreou, Alan L. Yuille
ISBI
2009
IEEE
14 years 3 months ago
Instance-Based Generative Biological Shape Modeling
Biological shape modeling is an essential task that is required for systems biology efforts to simulate complex cell behaviors. Statistical learning methods have been used to buil...
Tao Peng, Wei Wang, Gustavo K. Rohde, Robert F. Mu...
ICML
2003
IEEE
14 years 9 months ago
Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning
We present a novel Bayesian approach to the problem of value function estimation in continuous state spaces. We define a probabilistic generative model for the value function by i...
Yaakov Engel, Shie Mannor, Ron Meir