Label-Embedding for Attribute-Based Classification

Zeynep Akata 1, 2 Florent Perronnin 1 Zaid Harchaoui 2 Cordelia Schmid 2
1 Xerox Research Centre Europe [Meylan]
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, which enables 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 classes rank higher than the incorrect ones. Results on the Animals With Attributes and Caltech-UCSD-Birds datasets show that the proposed framework outperforms the standard Direct Attribute Prediction baseline in a zero-shot learning scenario. The label embedding framework offers other advantages such as the ability to leverage alternative sources of information in addition to attributes (e.g. class hierarchies) or to transition smoothly from zero-shot learning to learning with large quantities of data.
Type de document :
Communication dans un congrès
CVPR - IEEE Computer Vision and Pattern Recognition, Jun 2013, Portland, United States. IEEE, pp.819-826, 2013, 〈10.1109/CVPR.2013.111〉
Liste complète des métadonnées

Littérature citée [43 références]  Voir  Masquer  Télécharger

methodology.png
methodology.png
https://hal.inria.fr/hal-00815747
Contributeur : Thoth Team <>
Soumis le : jeudi 11 juillet 2013 - 19:14:24
Dernière modification le : mercredi 11 avril 2018 - 01:59:34
Document(s) archivé(s) le : mercredi 5 avril 2017 - 10:07:05

Fichiers

PID2749989.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

INRIA | LJK_GI_LEAR | LJK_GI | LJK | UGA

Citation

Zeynep Akata, Florent Perronnin, Zaid Harchaoui, Cordelia Schmid. Label-Embedding for Attribute-Based Classification. CVPR - IEEE Computer Vision and Pattern Recognition, Jun 2013, Portland, United States. IEEE, pp.819-826, 2013, 〈10.1109/CVPR.2013.111〉. 〈hal-00815747v3〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

1319

Téléchargements de fichiers

1559

Contact


archive-ouverte@inria.fr