{"id":"https://openalex.org/W1931639407","doi":"https://doi.org/10.1109/cvpr.2015.7298754","title":"From captions to visual concepts and back","display_name":"From captions to visual concepts and back","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W1931639407","doi":"https://doi.org/10.1109/cvpr.2015.7298754","mag":"1931639407"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2015.7298754","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298754","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101866618","display_name":"Hao Fang","orcid":"https://orcid.org/0000-0002-5015-6017"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]},{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB","US"],"is_corresponding":true,"raw_author_name":"Hao Fang","raw_affiliation_strings":["Microsoft Research","University of Washington"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]},{"raw_affiliation_string":"University of Washington","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040211080","display_name":"Saurabh Gupta","orcid":"https://orcid.org/0000-0003-0095-4704"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]},{"id":"https://openalex.org/I2803209242","display_name":"University of California System","ror":"https://ror.org/00pjdza24","country_code":"US","type":"education","lineage":["https://openalex.org/I2803209242"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Saurabh Gupta","raw_affiliation_strings":["Microsoft Research","University of California"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]},{"raw_affiliation_string":"University of California","institution_ids":["https://openalex.org/I2803209242"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031485831","display_name":"Forrest Iandola","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]},{"id":"https://openalex.org/I2803209242","display_name":"University of California System","ror":"https://ror.org/00pjdza24","country_code":"US","type":"education","lineage":["https://openalex.org/I2803209242"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Forrest Iandola","raw_affiliation_strings":["Microsoft Research","University of California"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]},{"raw_affiliation_string":"University of California","institution_ids":["https://openalex.org/I2803209242"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072320253","display_name":"Rupesh K. Srivastava","orcid":"https://orcid.org/0000-0002-3323-0713"},"institutions":[{"id":"https://openalex.org/I2614128279","display_name":"Dalle Molle Institute for Artificial Intelligence Research","ror":"https://ror.org/013355g38","country_code":"CH","type":"facility","lineage":["https://openalex.org/I15196421","https://openalex.org/I2614128279","https://openalex.org/I57201433"]},{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]},{"id":"https://openalex.org/I15196421","display_name":"University of Applied Sciences and Arts of Southern Switzerland","ror":"https://ror.org/05ep8g269","country_code":"CH","type":"education","lineage":["https://openalex.org/I15196421"]}],"countries":["CH","GB"],"is_corresponding":false,"raw_author_name":"Rupesh K. Srivastava","raw_affiliation_strings":["IDSIA, USI-SUPSI, Berkeley","Microsoft Research"],"affiliations":[{"raw_affiliation_string":"IDSIA, USI-SUPSI, Berkeley","institution_ids":["https://openalex.org/I2614128279","https://openalex.org/I15196421"]},{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100671324","display_name":"Li Deng","orcid":"https://orcid.org/0000-0002-1014-0790"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Li Deng","raw_affiliation_strings":["Microsoft Research"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057866698","display_name":"Piotr Doll\u00e1r","orcid":null},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]},{"id":"https://openalex.org/I2252078561","display_name":"Meta (Israel)","ror":"https://ror.org/02388em19","country_code":"IL","type":"company","lineage":["https://openalex.org/I2252078561","https://openalex.org/I4210114444"]}],"countries":["IL","US"],"is_corresponding":false,"raw_author_name":"Piotr Dollar","raw_affiliation_strings":["Facebook AI Research","University of Washington"],"affiliations":[{"raw_affiliation_string":"Facebook AI Research","institution_ids":["https://openalex.org/I2252078561"]},{"raw_affiliation_string":"University of Washington","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047233371","display_name":"Jianfeng Gao","orcid":"https://orcid.org/0000-0002-6371-505X"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jianfeng Gao","raw_affiliation_strings":["Microsoft Research"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101727205","display_name":"Xiaodong He","orcid":"https://orcid.org/0000-0002-9463-9168"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Xiaodong He","raw_affiliation_strings":["Microsoft Research"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046235098","display_name":"Margaret Mitchell","orcid":"https://orcid.org/0000-0001-7043-6545"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Margaret Mitchell","raw_affiliation_strings":["Microsoft Research"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073032636","display_name":"John Platt","orcid":"https://orcid.org/0000-0002-5652-5303"},"institutions":[{"id":"https://openalex.org/I2803209242","display_name":"University of California System","ror":"https://ror.org/00pjdza24","country_code":"US","type":"education","lineage":["https://openalex.org/I2803209242"]},{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John C. Platt","raw_affiliation_strings":["Google","University of California"],"affiliations":[{"raw_affiliation_string":"Google","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"University of California","institution_ids":["https://openalex.org/I2803209242"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058450549","display_name":"C. Lawrence Zitnick","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"C. Lawrence Zitnick","raw_affiliation_strings":["Microsoft Research"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069954850","display_name":"Geoffrey Zweig","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Geoffrey Zweig","raw_affiliation_strings":["Microsoft Research"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":12,"corresponding_author_ids":["https://openalex.org/A5101866618"],"corresponding_institution_ids":["https://openalex.org/I201448701","https://openalex.org/I4210164937"],"apc_list":null,"apc_paid":null,"fwci":104.6425,"has_fulltext":false,"cited_by_count":1331,"citation_normalized_percentile":{"value":0.99957315,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1473","last_page":"1482"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8437687158584595},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7201201915740967},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.703748345375061},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5395670533180237},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5024034976959229},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4832378327846527},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.4805479645729065},{"id":"https://openalex.org/keywords/closed-captioning","display_name":"Closed captioning","score":0.47675830125808716},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.46870988607406616},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4480496048927307},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.4474395513534546},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.4397793114185333},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.42844098806381226},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.42354604601860046},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3516530394554138},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.29406607151031494},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.11625584959983826}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8437687158584595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7201201915740967},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.703748345375061},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5395670533180237},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5024034976959229},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4832378327846527},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.4805479645729065},{"id":"https://openalex.org/C157657479","wikidata":"https://www.wikidata.org/wiki/Q2367247","display_name":"Closed captioning","level":3,"score":0.47675830125808716},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.46870988607406616},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4480496048927307},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4474395513534546},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.4397793114185333},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.42844098806381226},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.42354604601860046},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3516530394554138},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.29406607151031494},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.11625584959983826},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr.2015.7298754","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298754","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6700000166893005,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":98,"referenced_works":["https://openalex.org/W7746136","https://openalex.org/W8316075","https://openalex.org/W68733909","https://openalex.org/W154472438","https://openalex.org/W1512387364","https://openalex.org/W1514535095","https://openalex.org/W1686810756","https://openalex.org/W1687846465","https://openalex.org/W1828348983","https://openalex.org/W1858383477","https://openalex.org/W1861492603","https://openalex.org/W1889081078","https://openalex.org/W1895577753","https://openalex.org/W1895989618","https://openalex.org/W1897761818","https://openalex.org/W1905882502","https://openalex.org/W1947481528","https://openalex.org/W1956340063","https://openalex.org/W1964763677","https://openalex.org/W1965154800","https://openalex.org/W1970207841","https://openalex.org/W1987835821","https://openalex.org/W1996418862","https://openalex.org/W2031004336","https://openalex.org/W2031489346","https://openalex.org/W2062118960","https://openalex.org/W2066134726","https://openalex.org/W2081613070","https://openalex.org/W2088049833","https://openalex.org/W2091812280","https://openalex.org/W2096175520","https://openalex.org/W2101105183","https://openalex.org/W2102439588","https://openalex.org/W2102605133","https://openalex.org/W2108325777","https://openalex.org/W2108598243","https://openalex.org/W2109586012","https://openalex.org/W2110951295","https://openalex.org/W2112912048","https://openalex.org/W2119775030","https://openalex.org/W2120615054","https://openalex.org/W2120861206","https://openalex.org/W2123024445","https://openalex.org/W2123301721","https://openalex.org/W2128856065","https://openalex.org/W2131876387","https://openalex.org/W2136189984","https://openalex.org/W2146574666","https://openalex.org/W2149172860","https://openalex.org/W2149557440","https://openalex.org/W2154318594","https://openalex.org/W2155541015","https://openalex.org/W2155893237","https://openalex.org/W2159243025","https://openalex.org/W2161381512","https://openalex.org/W2163605009","https://openalex.org/W2166010828","https://openalex.org/W2171361956","https://openalex.org/W2201007611","https://openalex.org/W2508429489","https://openalex.org/W2949447259","https://openalex.org/W2949465329","https://openalex.org/W2951183276","https://openalex.org/W2952072685","https://openalex.org/W2962835968","https://openalex.org/W4239072543","https://openalex.org/W6600313631","https://openalex.org/W6630875275","https://openalex.org/W6637306801","https://openalex.org/W6637373629","https://openalex.org/W6638549007","https://openalex.org/W6639102338","https://openalex.org/W6639118148","https://openalex.org/W6639425484","https://openalex.org/W6639432524","https://openalex.org/W6639657675","https://openalex.org/W6639694449","https://openalex.org/W6639809013","https://openalex.org/W6640617836","https://openalex.org/W6641064462","https://openalex.org/W6658109957","https://openalex.org/W6674650171","https://openalex.org/W6676297131","https://openalex.org/W6676497082","https://openalex.org/W6676647902","https://openalex.org/W6677994088","https://openalex.org/W6678040779","https://openalex.org/W6678262379","https://openalex.org/W6680450716","https://openalex.org/W6682059829","https://openalex.org/W6682086108","https://openalex.org/W6683033130","https://openalex.org/W6683512859","https://openalex.org/W6684191040","https://openalex.org/W6684369376","https://openalex.org/W6685230081","https://openalex.org/W6687543112","https://openalex.org/W6898505805"],"related_works":["https://openalex.org/W4210416330","https://openalex.org/W3088136942","https://openalex.org/W2949362007","https://openalex.org/W2775506363","https://openalex.org/W4290852288","https://openalex.org/W4388893791","https://openalex.org/W4283207562","https://openalex.org/W2963177403","https://openalex.org/W2330246314","https://openalex.org/W2047632477"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,22,64,73,99,115],"novel":[4],"approach":[5],"for":[6,36],"automatically":[7],"generating":[8],"image":[9,25,78],"descriptions:":[10],"visual":[11,34],"detectors,":[12],"language":[13,66,69],"models,":[14],"and":[15,53,98],"multimodal":[16,101],"similarity":[17,102],"models":[18],"learnt":[19],"directly":[20],"from":[21,72],"dataset":[23],"of":[24,47,75,84,118,147],"captions.":[26],"We":[27,87],"use":[28],"multiple":[29],"instance":[30],"learning":[31],"to":[32,63,80,127],"train":[33],"detectors":[35],"words":[37],"that":[38],"commonly":[39],"occur":[40],"in":[41],"captions,":[42],"including":[43],"many":[44],"different":[45],"parts":[46],"speech":[48],"such":[49],"as":[50,60],"nouns,":[51],"verbs,":[52],"adjectives.":[54],"The":[55,68],"word":[56,85],"detector":[57],"outputs":[58],"serve":[59],"conditional":[61],"inputs":[62],"maximum-entropy":[65],"model.":[67,103],"model":[70],"learns":[71],"set":[74],"over":[76],"400,000":[77],"descriptions":[79],"capture":[81,88],"the":[82,109,124,138,148],"statistics":[83],"usage.":[86],"global":[89],"semantics":[90],"by":[91,130],"re-ranking":[92],"caption":[93],"candidates":[94],"using":[95],"sentence-level":[96],"features":[97],"deep":[100],"Our":[104],"system":[105,125,139],"is":[106],"state-of-the-art":[107],"on":[108,133],"official":[110],"Microsoft":[111],"COCO":[112],"benchmark,":[113],"producing":[114],"BLEU-4":[116],"score":[117],"29.1%.":[119],"When":[120],"human":[121],"judges":[122],"compare":[123],"captions":[126,140],"ones":[128],"written":[129],"other":[131],"people":[132],"our":[134],"held-out":[135],"test":[136],"set,":[137],"have":[141],"equal":[142],"or":[143],"better":[144],"quality":[145],"34%":[146],"time.":[149]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":26},{"year":2024,"cited_by_count":70},{"year":2023,"cited_by_count":65},{"year":2022,"cited_by_count":82},{"year":2021,"cited_by_count":149},{"year":2020,"cited_by_count":153},{"year":2019,"cited_by_count":222},{"year":2018,"cited_by_count":200},{"year":2017,"cited_by_count":172},{"year":2016,"cited_by_count":124},{"year":2015,"cited_by_count":63},{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}