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» Learning Object Representations Using Sequential Patterns
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ICML
2005
IEEE
14 years 8 months ago
Q-learning of sequential attention for visual object recognition from informative local descriptors
This work provides a framework for learning sequential attention in real-world visual object recognition, using an architecture of three processing stages. The first stage rejects...
Lucas Paletta, Gerald Fritz, Christin Seifert
VTS
2000
IEEE
113views Hardware» more  VTS 2000»
13 years 12 months ago
Hidden Markov and Independence Models with Patterns for Sequential BIST
We propose a novel BIST technique for non-scan sequential circuits which does not modify the circuit under test. It uses a learning algorithm to build a hardware test sequence gen...
Laurent Bréhélin, Olivier Gascuel, G...
CVPR
2008
IEEE
14 years 9 months ago
Latent topic random fields: Learning using a taxonomy of labels
An important problem in image labeling concerns learning with images labeled at varying levels of specificity. We propose an approach that can incorporate images with labels drawn...
Xuming He, Richard S. Zemel
NN
2008
Springer
201views Neural Networks» more  NN 2008»
13 years 7 months ago
Learning representations for object classification using multi-stage optimal component analysis
Learning data representations is a fundamental challenge in modeling neural processes and plays an important role in applications such as object recognition. In multi-stage Optima...
Yiming Wu, Xiuwen Liu, Washington Mio
BC
2005
68views more  BC 2005»
13 years 7 months ago
Learning viewpoint invariant object representations using a temporal coherence principle
Wolfgang Einhäuser, Jörg Hipp, Julian Eg...