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FLAIRS
2009
15 years 16 days ago
Mapping Grounded Object Properties across Perceptually Heterogeneous Embodiments
As robots become more common, it becomes increasingly useful for them to communicate and effectively share knowledge that they have learned through their individual experiences. L...
Zsolt Kira
158
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SDM
2011
SIAM
233views Data Mining» more  SDM 2011»
14 years 5 months ago
Multi-Instance Mixture Models
Multi-instance (MI) learning is a variant of supervised learning where labeled examples consist of bags (i.e. multi-sets) of feature vectors instead of just a single feature vecto...
James R. Foulds, Padhraic Smyth
128
Voted
CVPR
2008
IEEE
16 years 4 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 ...
ICCV
2009
IEEE
16 years 7 months ago
What is the Best Multi-Stage Architecture for Object Recognition?
In many recent object recognition systems, feature extraction stages are generally composed of a filter bank, a non-linear transformation, and some sort of feature pooling layer...
Kevin Jarrett, Koray Kavukcuoglu, Marc’Aurelio R...
149
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CIVR
2010
Springer
199views Image Analysis» more  CIVR 2010»
15 years 7 months ago
Unsupervised multi-feature tag relevance learning for social image retrieval
Interpreting the relevance of a user-contributed tag with respect to the visual content of an image is an emerging problem in social image retrieval. In the literature this proble...
Xirong Li, Cees G. M. Snoek, Marcel Worring