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CVPR
2008
IEEE
16 years 5 months ago
Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation and recognition
We present a new unsupervised method to learn unified probabilistic object models (POMs) which can be applied to classification, segmentation, and recognition. We formulate this a...
Yuanhao Chen, Long Zhu, Alan L. Yuille, HongJiang ...
CVPR
2007
IEEE
16 years 5 months ago
OPTIMOL: automatic Online Picture collecTion via Incremental MOdel Learning
A well-built dataset is a necessary starting point for advanced computer vision research. It plays a crucial role in evaluation and provides a continuous challenge to stateof-the-...
Li-Jia Li, Gang Wang, Fei-Fei Li 0002
AAAI
2007
15 years 6 months ago
Learning Large Scale Common Sense Models of Everyday Life
Recent work has shown promise in using large, publicly available, hand-contributed commonsense databases as joint models that can be used to infer human state from day-to-day sens...
William Pentney, Matthai Philipose, Jeff A. Bilmes...
CSCL
2006
109views more  CSCL 2006»
15 years 3 months ago
Supporting synchronous collaborative learning: A generic, multi-dimensional model
Future CSCL technologies are described by the community as flexible, tailorable, negotiable, and appropriate for various collaborative settings, conditions and contexts. This paper...
Jacques Lonchamp
DAGM
2006
Springer
15 years 7 months ago
On-Line, Incremental Learning of a Robust Active Shape Model
Abstract. Active Shape Models are commonly used to recognize and locate different aspects of known rigid objects. However, they require an off-line learning stage, such that the ex...
Michael Fussenegger, Peter M. Roth, Horst Bischof,...