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» Unsupervised Learning of Models for Recognition
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NN
2002
Springer
114views Neural Networks» more  NN 2002»
14 years 9 months ago
Learning the parts of objects by auto-association
Recognition-by-components is one of the possible strategies proposed for object recognition by the brain, but little is known about the low-level mechanism by which the parts of o...
Xijin Ge, Shuichi Iwata
ICCV
2005
IEEE
15 years 12 months ago
Learning Object Categories from Google's Image Search
Current approaches to object category recognition require datasets of training images to be manually prepared, with varying degrees of supervision. We present an approach that can...
Robert Fergus, Fei-Fei Li 0002, Pietro Perona, And...
IROS
2009
IEEE
120views Robotics» more  IROS 2009»
15 years 4 months ago
Interactive learning of visually symmetric objects
— This paper describes a robotic system that learns visual models of symmetric objects autonomously. Our robot learns by physically interacting with an object using its end effec...
Wai Ho Li, Lindsay Kleeman
NIPS
2008
14 years 11 months ago
Multi-Level Active Prediction of Useful Image Annotations for Recognition
We introduce a framework for actively learning visual categories from a mixture of weakly and strongly labeled image examples. We propose to allow the categorylearner to strategic...
Sudheendra Vijayanarasimhan, Kristen Grauman
CRV
2009
IEEE
115views Robotics» more  CRV 2009»
15 years 4 months ago
Learning Model Complexity in an Online Environment
In this paper we introduce the concept and method for adaptively tuning the model complexity in an online manner as more examples become available. Challenging classification pro...
Dan Levi, Shimon Ullman