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CVPR
2009
IEEE
1084views Computer Vision» more  CVPR 2009»
16 years 5 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...
ICCV
2011
IEEE
13 years 10 months ago
Relative Attributes
Human-nameable visual “attributes” can benefit various recognition tasks. However, existing techniques restrict these properties to categorical labels (for example, a person ...
Devi Parikh, Kristen Grauman
ICCV
2009
IEEE
16 years 2 months ago
Joint learning of visual attributes, object classes and visual saliency
We present a method to learn visual attributes (eg.“red”, “metal”, “spotted”) and object classes (eg. “car”, “dress”, “umbrella”) together. We assume imag...
Gang Wang, David Forsyth
ICML
2000
IEEE
15 years 10 months ago
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
Algorithms for feature selection fall into two broad categories: wrappers that use the learning algorithm itself to evaluate the usefulness of features and filters that evaluate f...
Mark A. Hall
WWW
2005
ACM
15 years 10 months ago
Learning domain ontologies for Web service descriptions: an experiment in bioinformatics
The reasoning tasks that can be performed with semantic web service descriptions depend on the quality of the domain ontologies used to create these descriptions. However, buildin...
Marta Sabou, Chris Wroe, Carole A. Goble, Gilad Mi...