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» Structured Learning and Prediction in Computer Vision
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CVPR
2010
IEEE
12 years 5 months ago
Abrupt motion tracking via adaptive stochastic approximation Monte Carlo sampling
Robust tracking of abrupt motion is a challenging task in computer vision due to the large motion uncertainty. In this paper, we propose a stochastic approximation Monte Carlo (...
Xiuzhuang Zhou and Yao Lu
JMLR
2008
230views more  JMLR 2008»
13 years 8 months ago
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of...
Michael Collins, Amir Globerson, Terry Koo, Xavier...
CVPR
2009
IEEE
15 years 3 months ago
Layered Graph Matching by Composite Cluster Sampling with Collaborative and Competitive Interactions
This paper studies a framework for matching an unknown number of corresponding structures in two images (shapes), motivated by detecting objects in cluttered background and lear...
Kun Zeng, Liang Lin, Song Chun Zhu, Xiaobai Liu
CVPR
2005
IEEE
14 years 10 months ago
Hallucinating Faces: TensorPatch Super-Resolution and Coupled Residue Compensation
In this paper, we propose a new face hallucination framework based on image patches, which integrates two novel statistical super-resolution models. Considering that image patches...
Wei Liu, Dahua Lin, Xiaoou Tang
AAAI
2007
13 years 10 months ago
A Randomized String Kernel and Its Application to RNA Interference
String kernels directly model sequence similarities without the necessity of extracting numerical features in a vector space. Since they better capture complex traits in the seque...
Shibin Qiu, Terran Lane, Ljubomir J. Buturovic