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» Efficient Piecewise Learning for Conditional Random Fields
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189
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PAMI
2010
417views more  PAMI 2010»
15 years 1 months ago
Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation
The notion of using context information for solving high-level vision and medical image segmentation problems has been increasingly realized in the field. However, how to learn a...
Zhuowen Tu, Xiang Bai
125
Voted
ECCV
2008
Springer
16 years 4 months ago
Fast Automatic Single-View 3-d Reconstruction of Urban Scenes
We consider the problem of estimating 3-d structure from a single still image of an outdoor urban scene. Our goal is to efficiently create 3-d models which are visually pleasant. W...
Olga Barinova, Vadim Konushin, Anton Yakubenko, Ke...
119
Voted
ACL
2010
15 years 20 days ago
Practical Very Large Scale CRFs
Conditional Random Fields (CRFs) are a widely-used approach for supervised sequence labelling, notably due to their ability to handle large description spaces and to integrate str...
Thomas Lavergne, Olivier Cappé, Franç...
117
Voted
ICDM
2007
IEEE
198views Data Mining» more  ICDM 2007»
15 years 9 months ago
Social Network Extraction of Academic Researchers
This paper addresses the issue of extraction of an academic researcher social network. By researcher social network extraction, we are aimed at finding, extracting, and fusing the...
Jie Tang, Duo Zhang, Limin Yao
135
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ACL
2009
15 years 6 days ago
Annotating and Recognising Named Entities in Clinical Notes
This paper presents ongoing research in clinical information extraction. This work introduces a new genre of text which are not well-written, noise prone, ungrammatical and with m...
Yefeng Wang