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» Learning the Compositional Nature of Visual Objects
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ICCV
2007
IEEE
14 years 9 months ago
Active Learning with Gaussian Processes for Object Categorization
Discriminative methods for visual object category recognition are typically non-probabilistic, predicting class labels but not directly providing an estimate of uncertainty. Gauss...
Ashish Kapoor, Kristen Grauman, Raquel Urtasun, Tr...
CVPR
2010
IEEE
14 years 4 months ago
Tracking the Invisible: Learning Where the Object Might be
Objects are usually embedded into context. Visual context has been successfully used in object detection tasks, however, it is often ignored in object tracking. We propose a metho...
Helmut Grabner, Jiri Matas, Philippe Cattin, Luc V...
IJCV
2007
196views more  IJCV 2007»
13 years 7 months ago
Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition
We investigate a method for learning object categories in a weakly supervised manner. Given a set of images known to contain the target category from a similar viewpoint, learning...
Robert Fergus, Pietro Perona, Andrew Zisserman
ICCV
2005
IEEE
14 years 9 months ago
A Maximum Entropy Framework for Part-Based Texture and Object Recognition
This paper presents a probabilistic part-based approach for texture and object recognition. Textures are represented using a part dictionary found by quantizing the appearance of ...
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
CVPR
2008
IEEE
14 years 9 months ago
Similarity-based cross-layered hierarchical representation for object categorization
This paper proposes a new concept in hierarchical representations that exploits features of different granularity and specificity coming from all layers of the hierarchy. The conc...
Sanja Fidler, Marko Boben, Ales Leonardis