{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T22:31:48Z","timestamp":1783377108694,"version":"3.54.6"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>This paper investigates a challenging problem,which is known as fine-grained image classification(FGIC). Different from conventional computer visionproblems, FGIC suffers from the large intraclassdiversities and subtle inter-class differences.Existing FGIC approaches are limited to exploreonly the visual information embedded in the images.In this paper, we present a novel approachwhich can use handy prior knowledge from eitherstructured knowledge bases or unstructured text tofacilitate FGIC. Specifically, we propose a visual-semanticembedding model which explores semanticembedding from knowledge bases and text, andfurther trains a novel end-to-end CNN frameworkto linearly map image features to a rich semanticembedding space. Experimental results on a challenginglarge-scale UCSD Bird-200-2011 datasetverify that our approach outperforms several state-of-the-art methods with significant advances.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/145","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:49:10Z","timestamp":1530769750000},"page":"1043-1049","source":"Crossref","is-referenced-by-count":36,"title":["Fine-grained Image Classification by  Visual-Semantic Embedding"],"prefix":"10.24963","author":[{"given":"Huapeng","family":"Xu","sequence":"first","affiliation":[{"name":"Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guilin","family":"Qi","sequence":"additional","affiliation":[{"name":"Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Li","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chendu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Wang","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong University, Xi'an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang","family":"Xu","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Gao","sequence":"additional","affiliation":[{"name":"Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","theme":"Artificial Intelligence","location":"Stockholm, Sweden","acronym":"IJCAI-2018","number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2018,7,13]]},"end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:50:21Z","timestamp":1530769821000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/145"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/145","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}