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» Learning Mid-Level Features For Recognition
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ICPR
2010
IEEE
14 years 2 months ago
Object Recognition and Localization Via Spatial Instance Embedding
—We propose an approach for improving object recognition and localization using spatial kernels together with instance embedding. Our approach treats each image as a bag of insta...
Nazli Ikizler Cinbis, Stan Sclaroff
NN
2002
Springer
114views Neural Networks» more  NN 2002»
13 years 7 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
NAACL
2010
13 years 5 months ago
Learning about Voice Search for Spoken Dialogue Systems
In a Wizard-of-Oz experiment with multiple wizard subjects, each wizard viewed automated speech recognition (ASR) results for utterances whose interpretation is critical to task s...
Rebecca J. Passonneau, Susan L. Epstein, Tiziana L...
IROS
2006
IEEE
148views Robotics» more  IROS 2006»
14 years 1 months ago
Environment Understanding: Robust Feature Extraction from Range Sensor Data
— This paper proposes an approach allowing indoor environment supervised learning to recognize relevant features for environment understanding. Stochastic preprocessing methods i...
Antonio Romeo, Luis Montano
CVPR
2011
IEEE
13 years 3 months ago
Learning Object Color Models from Multi-view Constraints
Color is known to be highly discriminative for many object recognition tasks, but is difficult to infer from uncontrolled images in which the illuminant is not known. Traditional...
Trevor Owens, Kate Saenko, Trevor Darrell, Ayan Ch...