{"id":"https://openalex.org/W2963346784","doi":null,"title":"Deep Convolutional Ranking for Multilabel Image Annotation","display_name":"Deep Convolutional Ranking for Multilabel Image Annotation","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W2963346784","mag":"2963346784"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.749.3427","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.749.3427","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/1312.4894.pdf","raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1312.4894","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086635908","display_name":"Yunchao Gong","orcid":"https://orcid.org/0000-0002-9521-4655","observed_orcids":["https://orcid.org/0000-0002-9521-4655"]},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yunchao Gong","raw_affiliation_strings":["UNC-Chapel Hill"],"raw_orcid":"https://orcid.org/0000-0002-9521-4655","affiliations":[{"raw_affiliation_string":"UNC-Chapel Hill","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110220840","display_name":"Yangqing Jia","orcid":null,"observed_orcids":[]},"institutions":[{"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":"Yangqing Jia","raw_affiliation_strings":["Google Research,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Research,","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Thomas Leung","orcid":null,"observed_orcids":[]},"institutions":[{"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":"Thomas Leung","raw_affiliation_strings":["Google Research,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Research,","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064265174","display_name":"Alexander Toshev","orcid":"https://orcid.org/0000-0003-0925-638X","observed_orcids":["https://orcid.org/0000-0003-0925-638X"]},"institutions":[{"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":"Alexander Toshev","raw_affiliation_strings":["Google Research,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Research,","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032800189","display_name":"Sergey Ioffe","orcid":null,"observed_orcids":[]},"institutions":[{"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":"Sergey Ioffe","raw_affiliation_strings":["Google Research,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Research,","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":279,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.8119999766349792,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.8119999766349792,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.1160999983549118,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.05649999901652336,"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/deep-convolutional-features","display_name":"deep convolutional features","score":0.7440000176429749},{"id":"https://openalex.org/keywords/ranking-loss","display_name":"ranking loss","score":0.3880000114440918},{"id":"https://openalex.org/keywords/visual-feature-learning","display_name":"visual feature learning","score":0.43799999356269836}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8133248686790466},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7987210154533386},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7161380052566528},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6701921224594116},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.665778398513794},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5964223146438599},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5476011037826538},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5139038562774658},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.45399436354637146},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.4358977675437927},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43329918384552},{"id":"https://openalex.org/C199579030","wikidata":"https://www.wikidata.org/wiki/Q2851778","display_name":"Automatic image annotation","level":4,"score":0.4158391058444977},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4126785397529602},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3676089644432068},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.749.3427","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.749.3427","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/1312.4894.pdf","raw_type":"text"},{"id":"mag:2963346784","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1312.4894","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:2963346784","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1312.4894","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2535052028","https://openalex.org/W1487980087","https://openalex.org/W1706392676","https://openalex.org/W1051230991","https://openalex.org/W2903351005","https://openalex.org/W2241945224","https://openalex.org/W2788173335","https://openalex.org/W3128228480","https://openalex.org/W2258969189","https://openalex.org/W3040963348"],"abstract_inverted_index":{"Multilabel":[0],"image":[1],"annotation":[2],"is":[3],"one":[4],"of":[5,50],"the":[6,48,86,93,97,105,110],"most":[7],"important":[8],"challenges":[9],"in":[10,109],"computer":[11],"vision":[12],"with":[13,77],"many":[14],"real-world":[15],"applications.":[16],"While":[17],"existing":[18],"work":[19],"usually":[20],"use":[21],"con-ventional":[22],"visual":[23,99],"features":[24,28,52,100],"for":[25],"multilabel":[26,87],"annotation,":[27],"based":[29],"on":[30,92],"Deep":[31],"Neural":[32],"Networks":[33],"have":[34],"shown":[35],"potential":[36],"to":[37,46,59],"significantly":[38],"boost":[39],"performance.":[40],"In":[41],"this":[42],"work,":[43],"we":[44,63],"propose":[45],"leverage":[47],"advantage":[49],"such":[51],"and":[53],"analyze":[54],"key":[55],"compo-nents":[56],"that":[57,65],"lead":[58],"better":[60],"performances.":[61],"Specifically,":[62],"show":[64],"a":[66],"significant":[67],"per-formance":[68],"gain":[69],"could":[70],"be":[71],"obtained":[72],"by":[73,101],"combining":[74],"convolutional":[75],"architectures":[76],"approximate":[78],"top-k":[79],"ranking":[80],"objectives,":[81],"as":[82],"thye":[83],"naturally":[84],"fit":[85],"tagging":[88],"problem.":[89],"Our":[90],"experiments":[91],"NUS-WIDE":[94],"dataset":[95],"outperforms":[96],"conven-tional":[98],"about":[102],"10%,":[103],"obtaining":[104],"best":[106],"reported":[107],"performance":[108],"literature.":[111],"1":[112]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":15},{"year":2021,"cited_by_count":30},{"year":2020,"cited_by_count":32},{"year":2019,"cited_by_count":50},{"year":2018,"cited_by_count":44},{"year":2017,"cited_by_count":35},{"year":2016,"cited_by_count":22},{"year":2015,"cited_by_count":18},{"year":2014,"cited_by_count":2}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2019-07-30T00:00:00"}
