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» Online Multiple Instance Learning with No Regret
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CVPR
2012
IEEE
11 years 8 months ago
Batch mode Adaptive Multiple Instance Learning for computer vision tasks
Multiple Instance Learning (MIL) has been widely exploited in many computer vision tasks, such as image retrieval, object tracking and so on. To handle ambiguity of instance label...
Wen Li, Lixin Duan, Ivor Wai-Hung Tsang, Dong Xu
CVPR
2010
IEEE
14 years 1 months ago
On-line Semi-supervised Multiple-Instance Boosting
A recent dominating trend in tracking called tracking-by-detection uses on-line classifiers in order to redetect objects over succeeding frames. Although these methods usually deli...
Bernhard Zeisl, Christian Leistner, Amir Saffari, ...
CVPR
2009
IEEE
15 years 1 months ago
Visual Tracking with Online Multiple Instance Learning
In this paper, we address the problem of learning an adaptive appearance model for object tracking. In particular, a class of tracking techniques called “tracking by detection...
Boris Babenko, Ming-Hsuan Yang, Serge J. Belongie
PAMI
2011
12 years 8 months ago
Robust Object Tracking with Online Multiple Instance Learning
In this paper we address the problem of tracking an object in a video given its location in the first frame and no other information. Recently, a class of tracking techniques cal...
Boris Babenko, Ming-Hsuan Yang, Serge Belongie
ICML
2009
IEEE
14 years 6 months ago
A simpler unified analysis of budget perceptrons
The kernel Perceptron is an appealing online learning algorithm that has a drawback: whenever it makes an error it must increase its support set, which slows training and testing ...
Ilya Sutskever