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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.<\/jats:p>","DOI":"10.1145\/2885252","type":"journal-article","created":{"date-parts":[[2016,2,26]],"date-time":"2016-02-26T14:29:03Z","timestamp":1456496943000},"page":"108-115","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Learning to name objects"],"prefix":"10.1145","volume":"59","author":[{"given":"Vicente","family":"Ordonez","sequence":"first","affiliation":[{"name":"Allen Institute for Artificial Intelligence, Seattle, WA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"University of North Carolina at Chapel Hill, NC"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Deng","sequence":"additional","affiliation":[{"name":"University of Michigan, Ann Arbor, MI"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yejin","family":"Choi","sequence":"additional","affiliation":[{"name":"University of Washington, Seattle, WA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander C.","family":"Berg","sequence":"additional","affiliation":[{"name":"University of North Carolina at Chapel Hill, NC"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tamara L.","family":"Berg","sequence":"additional","affiliation":[{"name":"University of North Carolina at Chapel Hill, NC"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2016,2,25]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.3115\/1225403.1225421"},{"key":"e_1_2_1_2_1","volume-title":"Web 1t 5-gram version 1","author":"Brants T.","year":"2006","unstructured":"Brants , T. , Franz , A. 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Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T. Decaf: A deep convolutional activation feature for generic visual recognition, 2013. arXiv preprint arXiv:1310.1531."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/1888089.1888092"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7287.001.0001"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2009.167"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/2566972.2566993"},{"key":"e_1_2_1_12_1","volume-title":"Caffe: An open source convolutional architecture for fast feature embedding","author":"Jia Y.","year":"2013","unstructured":"Jia , Y. Caffe: An open source convolutional architecture for fast feature embedding , 2013 . http:\/\/caffe.berkeleyvision.org\/. Jia, Y. 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(2012), Curran Associates, Inc., 1097--1105."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.162"},{"key":"e_1_2_1_16_1","volume-title":"Association for Computational Linguistics (ACL)","author":"Kuznetsova P.","year":"2012","unstructured":"Kuznetsova , P. , Ordonez , V. , Berg , A. , Berg , T.L. , Choi , Y. Collective generation of natural image descriptions . In Association for Computational Linguistics (ACL) , 2012 . Kuznetsova, P., Ordonez, V., Berg, A., Berg, T.L., Choi, Y. Collective generation of natural image descriptions. In Association for Computational Linguistics (ACL), 2012."},{"key":"e_1_2_1_17_1","first-page":"1","article-title":"Composition and compression of trees for image descriptions","volume":"2","author":"Kuznetsova P.","year":"2014","unstructured":"Kuznetsova , P. , Ordonez , V. , Berg , T. , Choi , Y. Treetalk : Composition and compression of trees for image descriptions . Trans. Assoc. Comput. Linguist. 2 , 1 ( 2014 ), 351--362. Kuznetsova, P., Ordonez, V., Berg, T., Choi, Y. Treetalk: Composition and compression of trees for image descriptions. Trans. Assoc. Comput. Linguist. 2, 1 (2014), 351--362.","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"e_1_2_1_18_1","volume-title":"Proceedings of the 29th International Conference on Machine Learning (ICML12)","author":"Le Q.","year":"2012","unstructured":"Le , Q. , Ranzato , M. , Monga , R. , Devin , M. , Chen , K. , Corrado , G. , Dean , J. , Ng , A. Building high-level features using large scale unsupervised learning . In Proceedings of the 29th International Conference on Machine Learning (ICML12) , John Langford and Joelle Pineau, eds. (Edinburgh, Scotland, GB , July 2012 ), Omnipress, New York, NY, USA, 81--88. Le, Q., Ranzato, M., Monga, R., Devin, M., Chen, K., Corrado, G., Dean, J., Ng, A. Building high-level features using large scale unsupervised learning. 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