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» Learning Flexible Features for Conditional Random Fields
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ICASSP
2011
IEEE
14 years 1 months ago
Using residual vector quantization for image content classification
Multistage residual vector quantizers (RVQ) with optimal direct sum decoder codebooks have been successfully designed and implemented for data compression. Due to its multistage s...
Syed Irteza Ali Khan, Christopher F. Barnes
CVPR
2008
IEEE
15 years 11 months ago
Max Margin AND/OR Graph learning for parsing the human body
We present a novel structure learning method, Max Margin AND/OR Graph (MM-AOG), for parsing the human body into parts and recovering their poses. Our method represents the human b...
Long Zhu, Yuanhao Chen, Yifei Lu, Chenxi Lin, Alan...
ELPUB
2006
ACM
15 years 3 months ago
Living Reviews - Innovative Resources for Scholarly Communication Bridging Diverse Spheres of Disciplines and Organisational Str
This contribution presents the concept and analyses the path of diffusion of an innovative publishing idea that originated in one speciality in physics and is now about to spread ...
Claus Dalchow, Michael Nentwich, Patrick Scherhauf...
CVPR
2005
IEEE
15 years 11 months ago
Cloth Representation by Shape from Shading with Shading Primitives
Cloth is a complex visual pattern with flexible 3D shape and illumination variations. Computing the 3D shape of cloth from a single image is of great interest to both computer gra...
Feng Han, Song Chun Zhu
IJCAI
2007
14 years 11 months ago
Simple Training of Dependency Parsers via Structured Boosting
Recently, significant progress has been made on learning structured predictors via coordinated training algorithms such as conditional random fields and maximum margin Markov ne...
Qin Iris Wang, Dekang Lin, Dale Schuurmans