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» Learning the Structure of Linear Latent Variable Models
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CVPR
2010
IEEE
14 years 3 months ago
Automatic Discovery of Meaningful Object Parts with Latent CRFs
Object recognition is challenging due to high intra-class variability caused, e.g., by articulation, viewpoint changes, and partial occlusion. Successful methods need to strike a...
Paul Schnitzspan, Stefan Roth, Bernt Schiele
ICPR
2008
IEEE
14 years 1 months ago
Manifold denoising with Gaussian Process Latent Variable Models
For a finite set of points lying on a lower dimensional manifold embedded in a high-dimensional data space, algorithms have been developed to study the manifold structure. Howeve...
Yan Gao, Kap Luk Chan, Wei-Yun Yau
CVPR
2008
IEEE
14 years 9 months ago
Context and observation driven latent variable model for human pose estimation
Current approaches to pose estimation and tracking can be classified into two categories: generative and discriminative. While generative approaches can accurately determine human...
Abhinav Gupta, Trista Chen, Francine Chen, Don Kim...
TOG
2012
255views Communications» more  TOG 2012»
11 years 10 months ago
A probabilistic model for component-based shape synthesis
We present an approach to synthesizing shapes from complex domains, by identifying new plausible combinations of components from existing shapes. Our primary contribution is a new...
Evangelos Kalogerakis, Siddhartha Chaudhuri, Daphn...
PRICAI
2000
Springer
13 years 11 months ago
Generating Hierarchical Structure in Reinforcement Learning from State Variables
This paper presents the CQ algorithm which decomposes and solves a Markov Decision Process (MDP) by automatically generating a hierarchy of smaller MDPs using state variables. The ...
Bernhard Hengst