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» Learning in Computer Vision: Some Thoughts
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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 1 months ago
Trajectory Series Analysis based Event Rule Induction for Visual Surveillance
In this paper, a generic rule induction framework based on trajectory series analysis is proposed to learn the event rules. First the trajectories acquired by a tracking system ar...
Zhang Zhang, Kaiqi Huang, Tieniu Tan, Liangsheng W...
ICCV
2011
IEEE
12 years 7 months ago
Data-driven Crowd Analysis in Videos
In this work we present a new crowd analysis algorithm powered by behavior priors that are learned on a large database of crowd videos gathered from the Internet. The algorithm wo...
Mikel Rodriguez, Josef Sivic, Ivan Laptev, Jean-Yv...
CVPR
2009
IEEE
13 years 10 months ago
Robust unsupervised segmentation of degraded document images with topic models
Segmentation of document images remains a challenging vision problem. Although document images have a structured layout, capturing enough of it for segmentation can be difficult....
Timothy J. Burns, Jason J. Corso
CORR
2012
Springer
170views Education» more  CORR 2012»
12 years 3 months ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson