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» Training of Classifiers Using Virtual Samples Only
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ECCV
2010
Springer
13 years 10 months ago
Semantic Segmentation of Urban Scenes Using Dense Depth Maps
In this paper we present a framework for semantic scene parsing and object recognition based on dense depth maps. Five viewindependent 3D features that vary with object class are e...
Chenxi Zhang, Liang Wang, Ruigang Yang
NAACL
2010
13 years 6 months ago
Learning Translation Boundaries for Phrase-Based Decoding
Constrained decoding is of great importance not only for speed but also for translation quality. Previous efforts explore soft syntactic constraints which are based on constituent...
Deyi Xiong, Min Zhang, Haizhou Li
CVPR
2007
IEEE
14 years 11 months ago
Learning Features for Tracking
We treat tracking as a matching problem of detected keypoints between successive frames. The novelty of this paper is to learn classifier-based keypoint descriptions allowing to i...
Michael Grabner, Helmut Grabner, Horst Bischof

Publication
922views
15 years 4 months ago
Multi-Class Active Learning for Image Classification
One of the principal bottlenecks in applying learning techniques to classification problems is the large amount of labeled training data required. Especially for images and video, ...
Ajay J. Joshi, Fatih Porikli, Nikolaos Papanikolop...
IJCV
2012
11 years 11 months ago
Globally Optimal Estimation of Nonrigid Image Distortion
Abstract Image alignment in the presence of non-rigid distortions is a challenging task. Typically, this involves estimating the parameters of a dense deformation field that warps...
Yuandong Tian, Srinivasa G. Narasimhan