Attribute learning in large-scale datasets

Olga Russakovsky; Li Fei-Fei

doi:10.1007/978-3-642-35749-7_1

Attribute learning in large-scale datasets

Olga Russakovsky, Li Fei-Fei

Research output: Contribution to journal › Conference article › peer-review

35 Scopus citations

Abstract

We consider the task of learning visual connections between object categories using the ImageNet dataset, which is a large-scale dataset ontology containing more than 15 thousand object classes. We want to discover visual relationships between the classes that are currently missing (such as similar colors or shapes or textures). In this work we learn 20 visual attributes and use them in a zero-shot transfer learning experiment as well as to make visual connections between semantically unrelated object categories.

Original language	English (US)
Pages (from-to)	1-14
Number of pages	14
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6553 LNCS
Issue number	PART 1
DOIs	https://doi.org/10.1007/978-3-642-35749-7_1
State	Published - 2012
Externally published	Yes
Event	11th European Conference on Computer Vision, ECCV 2010 - Heraklion, Crete, Greece Duration: Sep 10 2010 → Sep 11 2010

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

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10.1007/978-3-642-35749-7_1

Cite this

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abstract = "We consider the task of learning visual connections between object categories using the ImageNet dataset, which is a large-scale dataset ontology containing more than 15 thousand object classes. We want to discover visual relationships between the classes that are currently missing (such as similar colors or shapes or textures). In this work we learn 20 visual attributes and use them in a zero-shot transfer learning experiment as well as to make visual connections between semantically unrelated object categories.",

author = "Olga Russakovsky and Li Fei-Fei",

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AB - We consider the task of learning visual connections between object categories using the ImageNet dataset, which is a large-scale dataset ontology containing more than 15 thousand object classes. We want to discover visual relationships between the classes that are currently missing (such as similar colors or shapes or textures). In this work we learn 20 visual attributes and use them in a zero-shot transfer learning experiment as well as to make visual connections between semantically unrelated object categories.

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JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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ER -

Attribute learning in large-scale datasets

Abstract

All Science Journal Classification (ASJC) codes

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