Attribute-Based Classification for Zero-Shot Visual Object Categorization

We study the problem of object recognition for categories for which we have no training examples, a task also called zero--data or zero-shot learning. This situation has hardly been studied in computer vision research, even though it occurs frequently; the world contains tens of thousands of differe...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 36; H. 3; S. 453 - 465
Hauptverfasser:	Lampert, Christoph H., Nickisch, Hannes, Harmeling, Stefan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Los Alamitos, CA IEEE 01.03.2014 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Animals Applied sciences Artificial intelligence Classification Classification - methods Computer science; control theory; systems Computer vision Databases, Factual Exact sciences and technology Image Processing, Computer-Assisted - methods Learning Marine animals Models, Statistical Object recognition Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Probabilistic logic Programming languages Semantics Software Support Vector Machine Tasks Training Vectors vision and scene understanding Computer vision Zero-shot learning Image interpretation Image databank Pattern recognition Object recognition Annotation Semantics Scene analysis Animal vision and scene understanding Object oriented Collection Categorization Image classification
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:	Volltext
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