Attribute-Based Classification with Label-Embedding

Zeynep Akata 1, 2, * Florent Perronnin 1 Zaid Harchaoui 2 Cordelia Schmid 2
* Auteur correspondant
2 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : Attributes are an intermediate representation whose purpose is to enable parameter sharing between classes, a must when training data is scarce. We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors. We introduce a function which measures the compatibility between an image and a label embedding. The parameters of this function are learned on a training set of labeled samples to ensure that, given an image, the correct class has a higher compatibility than the incorrect ones. Experimental results on two standard image classification datasets are presented, resp. on the Animals With Attributes and on Caltech-UCSD-Birds datasets.
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Communication dans un congrès
NIPS 2013 Workshop on Output Representation Learning, Dec 2013, Lake Tahoe, United States. 2013
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Zeynep Akata, Florent Perronnin, Zaid Harchaoui, Cordelia Schmid. Attribute-Based Classification with Label-Embedding. NIPS 2013 Workshop on Output Representation Learning, Dec 2013, Lake Tahoe, United States. 2013. 〈hal-00903502〉

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