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How machines see the world: Understanding image annotation
Treccani Carloalberto
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peer-review
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Dive into the research topics of 'How machines see the world: Understanding image annotation'. Together they form a unique fingerprint.
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Keyphrases
Human Being
100%
Painters
100%
Cultural Contexts
100%
Image Annotation
100%
15th Century
100%
Learning Process
50%
Facebook
50%
Italy
50%
Image Recognition
50%
Historical Context
50%
Amazon
50%
Historical Events
50%
Quality Label
50%
Precarious Working Conditions
50%
Hand Position
50%
Michael Baxandall
50%
Arts and Humanities
Cultural Contexts
100%
Human Being
100%
15th century
100%
Historical Context
50%
Italy
50%
Facebook
50%
Working Conditions
50%
Inpainting
50%
Signification
50%
Learning Process
50%
Amazon
50%
Michael Baxandall
50%
Social Sciences
Cultural Context
100%
Human Being
100%
Facebook
50%
Italy
50%
Learning Process
50%
Working Conditions
50%
Earth and Planetary Sciences
Michael Reaction
100%
Italy
100%
Amazon
100%
Computer Science
Facebook
100%
Learning Process
100%
Image Annotation
100%
Psychology
Cultural Contexts
100%
Homo sapiens
100%
Facebook
50%