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» Learning in Computer Vision: Some Thoughts
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ECCV
2008
Springer
16 years 4 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
15 years 9 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
14 years 3 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
15 years 6 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»
13 years 10 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