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» Learning bilinear models for two-factor problems in vision
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CVPR
2012
IEEE
11 years 11 months ago
Unsupervised feature learning framework for no-reference image quality assessment
In this paper, we present an efficient general-purpose objective no-reference (NR) image quality assessment (IQA) framework based on unsupervised feature learning. The goal is to...
Peng Ye, Jayant Kumar, Le Kang, David S. Doermann
CVPR
2009
IEEE
15 years 4 months ago
Learning sign language by watching TV (using weakly aligned subtitles)
The goal of this work is to automatically learn a large number of British Sign Language (BSL) signs from TV broadcasts. We achieve this by using the supervisory information avai...
Patrick Buehler (University of Oxford), Mark Everi...
CVPR
2005
IEEE
14 years 11 months ago
Learning to Estimate Human Pose with Data Driven Belief Propagation
We propose a statistical formulation for 2-D human pose estimation from single images. The human body configuration is modeled by a Markov network and the estimation problem is to...
Gang Hua, Ming-Hsuan Yang, Ying Wu
CVPR
2010
IEEE
14 years 5 months ago
Learning Shift-Invariant Sparse Representation of Actions
A central problem in the analysis of motion capture (Mo- Cap) data is how to decompose motion sequences into primitives. Ideally, a description in terms of primitives should fac...
Yi Li
CVPR
2004
IEEE
14 years 11 months ago
Learning Classifiers from Imbalanced Data Based on Biased Minimax Probability Machine
We consider the problem of the binary classification on imbalanced data, in which nearly all the instances are labelled as one class, while far fewer instances are labelled as the...
Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. ...