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CVPR
2012
IEEE
13 years 2 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
2012
IEEE
13 years 2 months ago
RALF: A reinforced active learning formulation for object class recognition
Active learning aims to reduce the amount of labels required for classification. The main difficulty is to find a good trade-off between exploration and exploitation of the lab...
Sandra Ebert, Mario Fritz, Bernt Schiele
ICCV
1998
IEEE
16 years 1 months ago
Wormholes in Shape Space: Tracking Through Discontinuous Changes in Shape
Existing object tracking algorithms generally use some form of local optimisation, assuming that an object's position and shape change smoothly over time. In some situations ...
Tony Heap, David Hogg
86
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ECTEL
2007
Springer
15 years 5 months ago
Community Tools for Repurposing Learning Objects
A critical success factor for the reuse of learning objects is the ease by which they may be repurposed in order to enable reusability in a different teaching context from which th...
Chu Wang, Kate Dickens, Hugh C. Davis, Gary Wills
CVPR
2004
IEEE
16 years 1 months ago
Visual Tracking Using Learned Linear Subspaces
This paper presents a simple but robust visual tracking algorithm based on representing the appearances of objects using affine warps of learned linear subspaces of the image spac...
Jeffrey Ho, Kuang-Chih Lee, Ming-Hsuan Yang, David...