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Author Deng, Jia ♦ Berg, Tamara L. ♦ Choi, Yejin ♦ Liu, Wei ♦ Berg, Alexander C. ♦ Ordonez, Vicente
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Abstract We have seen remarkable recent progress in computational visual recognition, producing systems that can classify objects into thousands of different categories with increasing accuracy. However, one question that has received relatively less attention is "what labels should recognition systems output?" This paper looks at the problem of predicting category labels that mimic how human observers would name objects. This goal is related to the concept of entry-level categories first introduced by psychologists in the 1970s and 1980s. We extend these seminal ideas to study human naming at large scale and to learn computational models for predicting entry-level categories. Practical applications of this work include improving human-focused computer vision applications such as automatically generating a natural language description for an image or text-based image search.
Description Affiliation: University of North Carolina at Chapel Hill, NC (Liu, Wei; Berg, Alexander C.; Berg, Tamara L.) || University of Washington, Seattle, WA (Choi, Yejin) || Allen Institute for Artificial Intelligence, Seattle, WA (Ordonez, Vicente) || University of Michigan, Ann Arbor, MI (Deng, Jia)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2005-08-01
Publisher Place New York
Journal Communications of the ACM (CACM)
Volume Number 59
Issue Number 3
Page Count 8
Starting Page 108
Ending Page 115


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Source: ACM Digital Library