{"id":"https://openalex.org/W2016053056","doi":"https://doi.org/10.1109/cvpr.2014.223","title":"Large-Scale Video Classification with Convolutional Neural Networks","display_name":"Large-Scale Video Classification with Convolutional Neural Networks","publication_year":2014,"publication_date":"2014-06-01","ids":{"openalex":"https://openalex.org/W2016053056","doi":"https://doi.org/10.1109/cvpr.2014.223","mag":"2016053056"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2014.223","doi":"https://doi.org/10.1109/cvpr.2014.223","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2014.223","pdf_url":null,"source":{"id":"https://openalex.org/S4210176548","display_name":"IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings","issn_l":"1063-6919","issn":["1063-6919","2332-564X","2575-7075"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5009290031","display_name":"Andrej Karpathy","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"]},{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andrej Karpathy","raw_affiliation_strings":["Computer Science Department, Stanford University","Google Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, Stanford University","institution_ids":["https://openalex.org/I97018004"]},{"raw_affiliation_string":"Google Research","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078347851","display_name":"George D. Toderici","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":"George Toderici","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/A5048873857","display_name":"Sanketh Shetty","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":"Sanketh Shetty","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/A5066107323","display_name":"Rahul Sukthankar","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":"Rahul Sukthankar","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/A5100450462","display_name":"Li Fei-Fei","orcid":"https://orcid.org/0000-0002-7481-0810","observed_orcids":["https://orcid.org/0000-0002-7481-0810","https://orcid.org/0000-0001-7652-0473"]},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li Fei-Fei","raw_affiliation_strings":["Computer Science Department, Stanford University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, Stanford University","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":212.4645,"has_fulltext":false,"cited_by_count":6225,"citation_normalized_percentile":{"value":0.99972249,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1725","last_page":"1732"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.7026000022888184,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.7026000022888184,"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":0.25049999356269836,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.01510000042617321,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/video-classification","display_name":"video classification","score":0.9169999957084656},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.9160000085830688},{"id":"https://openalex.org/keywords/spatio-temporal-features","display_name":"spatio-temporal features","score":0.7519999742507935},{"id":"https://openalex.org/keywords/large-scale-video-datasets","display_name":"large-scale video datasets","score":0.6850000023841858},{"id":"https://openalex.org/keywords/youtube-videos","display_name":"YouTube videos","score":0.7419999837875366},{"id":"https://openalex.org/keywords/human-action-recognition","display_name":"human action recognition","score":0.6790000200271606},{"id":"https://openalex.org/keywords/ucf101","display_name":"UCF101","score":0.843999981880188},{"id":"https://openalex.org/keywords/training-efficiency","display_name":"training efficiency","score":0.5649999976158142},{"id":"https://openalex.org/keywords/generalization","display_name":"generalization","score":0.6740000247955322}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8293851613998413},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7817015647888184},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.692141056060791},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.590989887714386},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5153390765190125},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4847371280193329},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4600387215614319},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.45446649193763733},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4477701485157013},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.44267016649246216},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.4322255849838257},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.43065494298934937},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.43037131428718567},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40016481280326843},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3076260983943939},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06659528613090515},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C155202549","wikidata":"https://www.wikidata.org/wiki/Q178803","display_name":"International trade","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/cvpr.2014.223","doi":"https://doi.org/10.1109/cvpr.2014.223","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2014.223","pdf_url":null,"source":{"id":"https://openalex.org/S4210176548","display_name":"IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings","issn_l":"1063-6919","issn":["1063-6919","2332-564X","2575-7075"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.471.3312","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.471.3312","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.cmu.edu/~rahuls/pub/cvpr2014-deepvideo-rahuls.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.494.7098","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.494.7098","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://cs.stanford.edu/people/karpathy/deepvideo/deepvideo_cvpr2014.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.924.9361","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.924.9361","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Karpathy_Large-scale_Video_Classification_2014_CVPR_paper.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309398","display_name":"California Institute of Technology","ror":"https://ror.org/05dxps055"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W28988658","https://openalex.org/W237546731","https://openalex.org/W1498368596","https://openalex.org/W1586730761","https://openalex.org/W1967664674","https://openalex.org/W1993229407","https://openalex.org/W1999192586","https://openalex.org/W2020163092","https://openalex.org/W2022508996","https://openalex.org/W2062118960","https://openalex.org/W2100916003","https://openalex.org/W2102605133","https://openalex.org/W2108082645","https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2116456623","https://openalex.org/W2126574503","https://openalex.org/W2131846894","https://openalex.org/W2142194269","https://openalex.org/W2161969291","https://openalex.org/W2163605009","https://openalex.org/W2167510172","https://openalex.org/W2168231600","https://openalex.org/W2206858481","https://openalex.org/W2533739470","https://openalex.org/W2962820688","https://openalex.org/W2963038646","https://openalex.org/W2963542991","https://openalex.org/W4239072543","https://openalex.org/W6629098493","https://openalex.org/W6635099914","https://openalex.org/W6675296101","https://openalex.org/W6676297131","https://openalex.org/W6677681515","https://openalex.org/W6684191040","https://openalex.org/W6684372118","https://openalex.org/W6684859321"],"related_works":["https://openalex.org/W2952813363","https://openalex.org/W4378678253","https://openalex.org/W2911497689","https://openalex.org/W4360783045","https://openalex.org/W2770149305","https://openalex.org/W2972076240","https://openalex.org/W3167930666","https://openalex.org/W3014952856","https://openalex.org/W2964843961","https://openalex.org/W3010730661"],"abstract_inverted_index":{"Convolutional":[0],"Neural":[1],"Networks":[2],"(CNNs)":[3],"have":[4],"been":[5],"established":[6],"as":[7,73],"a":[8,34,55,69,74,100],"powerful":[9],"class":[10],"of":[11,27,37,54,63,77,117],"models":[12,107],"for":[13,50],"image":[14],"recognition":[15],"problems.":[16],"Encouraged":[17],"by":[18,121],"these":[19],"results,":[20],"we":[21],"provide":[22],"an":[23],"extensive":[24],"empirical":[25],"evaluation":[26],"CNNs":[28],"on":[29,126],"large-scale":[30],"video":[31],"classification":[32],"using":[33],"new":[35],"dataset":[36,131],"1":[38],"million":[39],"YouTube":[40],"videos":[41],"belonging":[42],"to":[43,60,91,96,105,109,138],"487":[44],"classes.":[45],"We":[46,111],"study":[47,113],"multiple":[48],"approaches":[49],"extending":[51],"the":[52,80,114,123,127,139],"connectivity":[53],"CNN":[56],"in":[57],"time":[58],"domain":[59],"take":[61],"advantage":[62],"local":[64],"spatio-temporal":[65,84],"information":[66],"and":[67,132],"suggest":[68],"multiresolution,":[70],"foveated":[71],"architecture":[72],"promising":[75],"way":[76],"speeding":[78],"up":[79,144],"training.":[81],"Our":[82],"best":[83,119],"networks":[85],"display":[86],"significant":[87,134],"performance":[88,116,135],"improvements":[89,136],"compared":[90,104,137],"strong":[92],"feature-based":[93],"baselines":[94],"(55.3%":[95],"63.9%),":[97],"but":[98],"only":[99],"surprisingly":[101],"modest":[102],"improvement":[103],"single-frame":[106],"(59.3%":[108],"60.9%).":[110],"further":[112],"generalization":[115],"our":[118],"model":[120,142],"retraining":[122],"top":[124],"layers":[125],"UCF-101":[128,140],"Action":[129],"Recognition":[130],"observe":[133],"baseline":[141],"(63.3%":[143],"from":[145],"43.9%).":[146]},"counts_by_year":[{"year":2026,"cited_by_count":100},{"year":2025,"cited_by_count":268},{"year":2024,"cited_by_count":344},{"year":2023,"cited_by_count":502},{"year":2022,"cited_by_count":561},{"year":2021,"cited_by_count":739},{"year":2020,"cited_by_count":871},{"year":2019,"cited_by_count":915},{"year":2018,"cited_by_count":745},{"year":2017,"cited_by_count":628},{"year":2016,"cited_by_count":364},{"year":2015,"cited_by_count":176},{"year":2014,"cited_by_count":11}],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2025-10-10T00:00:00"}