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ICCV
2005
IEEE
14 years 1 months ago
Learning Hierarchical Models of Scenes, Objects, and Parts
We describe a hierarchical probabilistic model for the detection and recognition of objects in cluttered, natural scenes. The model is based on a set of parts which describe the e...
Erik B. Sudderth, Antonio B. Torralba, William T. ...
FLAIRS
2001
13 years 9 months ago
Multiple Predicate Learning for Document Image Understanding
Documentimageunderstandingdenotesthe recognition of semanticallyrelevant componentsin the layout extracted froma documentimage.This recognitionprocessis based on somevisual models...
Floriana Esposito, Donato Malerba, Francesca A. Li...
ECCV
2008
Springer
14 years 9 months ago
Weakly Supervised Object Localization with Stable Segmentations
Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learni...
Carolina Galleguillos, Boris Babenko, Andrew Rabin...
CVPR
2007
IEEE
14 years 9 months ago
OPTIMOL: automatic Online Picture collecTion via Incremental MOdel Learning
A well-built dataset is a necessary starting point for advanced computer vision research. It plays a crucial role in evaluation and provides a continuous challenge to stateof-the-...
Li-Jia Li, Gang Wang, Fei-Fei Li 0002
CVPR
2007
IEEE
14 years 9 months ago
Objects in Action: An Approach for Combining Action Understanding and Object Perception
Analysis of videos of human-object interactions involves understanding human movements, locating and recognizing objects and observing the effects of human movements on those obje...
Abhinav Gupta, Larry S. Davis