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» Discriminative Mixed-Membership Models
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ICCV
2009
IEEE
16 years 11 months ago
Quantifying Contextual Information for Object Detection
Context is critical for minimising ambiguity in object de- tection. In this work, a novel context modelling framework is proposed without the need of any prior scene segmen- tat...
Wei-Shi Zheng, Shaogang Gong and Tao Xiang
CVPR
2006
IEEE
16 years 8 months ago
A Framework for Feature Selection for Background Subtraction
Background subtraction is a widely used paradigm to detect moving objects in video taken from a static camera and is used for various important applications such as video surveill...
Toufiq Parag, Ahmed M. Elgammal, Anurag Mittal
CVPR
2007
IEEE
16 years 8 months ago
Learning Visual Representations using Images with Captions
Current methods for learning visual categories work well when a large amount of labeled data is available, but can run into severe difficulties when the number of labeled examples...
Ariadna Quattoni, Michael Collins, Trevor Darrell
ICCV
2007
IEEE
16 years 8 months ago
3D generic object categorization, localization and pose estimation
We propose a novel and robust model to represent and learn generic 3D object categories. We aim to solve the problem of true 3D object categorization for handling arbitrary rotati...
Silvio Savarese, Fei-Fei Li 0002
ICCV
2001
IEEE
16 years 8 months ago
Separating Appearance from Deformation
By representing images and image prototypes by linear subspaces spanned by "tangent vectors" (derivatives of an image with respect to translation, rotation, etc.), impre...
Nebojsa Jojic, Patrice Simard, Brendan J. Frey, Da...