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» Learning Models for Object Recognition
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ECCV
2008
Springer
14 years 11 months ago
Beyond Nouns: Exploiting Prepositions and Comparative Adjectives for Learning Visual Classifiers
Learning visual classifiers for object recognition from weakly labeled data requires determining correspondence between image regions and semantic object classes. Most approaches u...
Abhinav Gupta, Larry S. Davis
EMNLP
2007
13 years 10 months ago
Semi-Supervised Structured Output Learning Based on a Hybrid Generative and Discriminative Approach
This paper proposes a framework for semi-supervised structured output learning (SOL), specifically for sequence labeling, based on a hybrid generative and discriminative approach...
Jun Suzuki, Akinori Fujino, Hideki Isozaki
DAGM
2008
Springer
13 years 10 months ago
A Probabilistic Segmentation Scheme
We propose a probabilistic segmentation scheme, which is widely applicable to some extend. Besides the segmentation itself our model incorporates object specific shading. Dependent...
Dmitrij Schlesinger, Boris Flach
JODL
2008
175views more  JODL 2008»
13 years 9 months ago
ALOCOM: a generic content model for learning objects
e-Learning organizations are focusing heavily on learning content reusability. The ultimate objective is a learning object economy characterized by searchable digital libraries of ...
Katrien Verbert, Erik Duval
CVPR
2012
IEEE
11 years 11 months ago
Pose pooling kernels for sub-category recognition
The ability to normalize pose based on super-category landmarks can significantly improve models of individual categories when training data are limited. Previous methods have co...
Ning Zhang, Ryan Farrell, Trevor Darrell