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CVPR
2009
IEEE
1084views Computer Vision» more  CVPR 2009»
15 years 4 months ago
Describing Objects by their Attributes
We propose to shift the goal of recognition from naming to describing. Doing so allows us not only to name familiar objects, but also: to report unusual aspects of a familiar ob...
Ali Farhadi, David A. Forsyth, Derek Hoiem, Ian En...
CVPR
2009
IEEE
15 years 4 months ago
What's It Going to Cost You?: Predicting Effort vs. Informativeness for Multi-Label Image Annotations
Active learning strategies can be useful when manual labeling effort is scarce, as they select the most informative examples to be annotated first. However, for visual category ...
Sudheendra Vijayanarasimhan (University of Texas a...
ICCV
2009
IEEE
15 years 2 months ago
Action Detection in Complex Scenes with Spatial and Temporal Ambiguities
In this paper, we investigate the detection of semantic human actions in complex scenes. Unlike conventional action recognition in well-controlled environments, action detection...
Yuxiao Hu, Liangliang Cao, Fengjun Lv, Shuicheng Y...
ICCV
2009
IEEE
15 years 2 months ago
Level Set Segmentation with Both Shape and Intensity Priors
We present a new variational level-set-based segmentation formulation that uses both shape and intensity prior information learned from a training set. By applying Bayes’ rule...
Siqi Chen and Richard J. Radke
CVPR
2000
IEEE
14 years 11 months ago
Towards Automatic Discovery of Object Categories
We propose a method to learn heterogeneous models of object classes for visual recognition. The training images contain a preponderance of clutter and learning is unsupervised. Ou...
Markus Weber, Max Welling, Pietro Perona