{"id":"https://openalex.org/W2087461551","doi":"https://doi.org/10.1145/2671188.2749406","title":"Evaluating Two-Stream CNN for Video Classification","display_name":"Evaluating Two-Stream CNN for Video Classification","publication_year":2015,"publication_date":"2015-06-22","ids":{"openalex":"https://openalex.org/W2087461551","doi":"https://doi.org/10.1145/2671188.2749406","mag":"2087461551"},"language":"en","primary_location":{"id":"doi:10.1145/2671188.2749406","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2671188.2749406","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th ACM on International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1504.01920","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100427158","display_name":"Hao Ye","orcid":"https://orcid.org/0000-0002-5939-4708"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hao Ye","raw_affiliation_strings":["Fudan University, Shanghai, China","Fudan University Shanghai, China#TAB#"],"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"Fudan University Shanghai, China#TAB#","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026167547","display_name":"Zuxuan Wu","orcid":"https://orcid.org/0000-0002-8689-5807"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zuxuan Wu","raw_affiliation_strings":["Fudan University, Shanghai, China","Fudan University Shanghai, China#TAB#"],"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"Fudan University Shanghai, China#TAB#","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100626035","display_name":"Rui-Wei Zhao","orcid":"https://orcid.org/0000-0002-8498-5761"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui-Wei Zhao","raw_affiliation_strings":["Fudan University, Shanghai, China","Fudan University Shanghai, China#TAB#"],"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"Fudan University Shanghai, China#TAB#","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101875912","display_name":"Xi Wang","orcid":"https://orcid.org/0000-0001-8918-7905"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xi Wang","raw_affiliation_strings":["Fudan University, Shanghai, China","Fudan University Shanghai, China#TAB#"],"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"Fudan University Shanghai, China#TAB#","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047962986","display_name":"Yu\u2013Gang Jiang","orcid":"https://orcid.org/0000-0002-1907-8567"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu-Gang Jiang","raw_affiliation_strings":["Fudan University, Shanghai, China","Fudan University Shanghai, China#TAB#"],"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"Fudan University Shanghai, China#TAB#","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003418019","display_name":"Xiangyang Xue","orcid":"https://orcid.org/0000-0002-4897-9209"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangyang Xue","raw_affiliation_strings":["Fudan University, Shanghai, China","Fudan University Shanghai, China#TAB#"],"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"Fudan University Shanghai, China#TAB#","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100427158"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":18.7414,"has_fulltext":false,"cited_by_count":111,"citation_normalized_percentile":{"value":0.99258134,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"435","last_page":"442"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994000196456909,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994000196456909,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.996999979019165,"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/T11439","display_name":"Video Analysis and Summarization","score":0.9898999929428101,"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":0.8772372007369995},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6531303524971008},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6328921318054199},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.621924877166748},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.612162709236145},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5746106505393982},{"id":"https://openalex.org/keywords/video-content-analysis","display_name":"Video content analysis","score":0.5259544849395752},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.5112349987030029},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48171335458755493},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4637722373008728},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3302992582321167},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3286525309085846},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.26961904764175415},{"id":"https://openalex.org/keywords/video-processing","display_name":"Video processing","score":0.26010841131210327}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8772372007369995},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6531303524971008},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6328921318054199},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.621924877166748},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.612162709236145},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5746106505393982},{"id":"https://openalex.org/C2778598663","wikidata":"https://www.wikidata.org/wiki/Q1407599","display_name":"Video content analysis","level":4,"score":0.5259544849395752},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.5112349987030029},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48171335458755493},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4637722373008728},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3302992582321167},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3286525309085846},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.26961904764175415},{"id":"https://openalex.org/C65483669","wikidata":"https://www.wikidata.org/wiki/Q3536669","display_name":"Video processing","level":2,"score":0.26010841131210327},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2671188.2749406","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2671188.2749406","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th ACM on International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1504.01920","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1504.01920","pdf_url":"https://arxiv.org/pdf/1504.01920","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1504.01920","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1504.01920","pdf_url":"https://arxiv.org/pdf/1504.01920","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5307376971","display_name":null,"funder_award_id":"61201387","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6363653248","display_name":null,"funder_award_id":"13PJ1400400, 13511504503","funder_id":"https://openalex.org/F4320321885","funder_display_name":"Science and Technology Commission of Shanghai Municipality"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321885","display_name":"Science and Technology Commission of Shanghai Municipality","ror":"https://ror.org/03kt66j61"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W237546731","https://openalex.org/W240069591","https://openalex.org/W276257457","https://openalex.org/W1423339008","https://openalex.org/W1686810756","https://openalex.org/W1871385855","https://openalex.org/W1947481528","https://openalex.org/W1965555842","https://openalex.org/W1983364832","https://openalex.org/W1989085630","https://openalex.org/W2016053056","https://openalex.org/W2019377328","https://openalex.org/W2024051019","https://openalex.org/W2062118960","https://openalex.org/W2069682406","https://openalex.org/W2076063813","https://openalex.org/W2082627290","https://openalex.org/W2093367888","https://openalex.org/W2097117768","https://openalex.org/W2102605133","https://openalex.org/W2105101328","https://openalex.org/W2112796928","https://openalex.org/W2120615054","https://openalex.org/W2130942839","https://openalex.org/W2131042978","https://openalex.org/W2136036867","https://openalex.org/W2136853139","https://openalex.org/W2141200610","https://openalex.org/W2142194269","https://openalex.org/W2147768505","https://openalex.org/W2153579005","https://openalex.org/W2156303437","https://openalex.org/W2163605009","https://openalex.org/W2163922914","https://openalex.org/W2164507085","https://openalex.org/W2310919327","https://openalex.org/W2949300694","https://openalex.org/W2949888546","https://openalex.org/W2950179405","https://openalex.org/W2951183276","https://openalex.org/W2951552696","https://openalex.org/W2952186347","https://openalex.org/W2964241990","https://openalex.org/W4248562487","https://openalex.org/W4294170691","https://openalex.org/W6600983433","https://openalex.org/W6609060481","https://openalex.org/W6656675045","https://openalex.org/W6669567237","https://openalex.org/W6674914833","https://openalex.org/W6679436768"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W3037187668","https://openalex.org/W4321487865","https://openalex.org/W4295036387","https://openalex.org/W2966313861","https://openalex.org/W3048359651","https://openalex.org/W2501889045","https://openalex.org/W2770139623","https://openalex.org/W4293137138","https://openalex.org/W1901380631"],"abstract_inverted_index":{"Videos":[0],"contain":[1],"very":[2,163,202],"rich":[3],"semantic":[4,65],"information.":[5],"Traditional":[6],"hand-crafted":[7],"features":[8],"are":[9,38,99,145,166],"known":[10],"to":[11,42,81,132,136,190],"be":[12],"inadequate":[13],"in":[14,29,70,187],"analyzing":[15,30],"complex":[16],"video":[17,49,59,95,171],"semantics.":[18],"Inspired":[19],"by":[20],"the":[21,25,43,52,89,127,155,161,177],"huge":[22],"success":[23],"of":[24,45,91,103],"deep":[26,46,92],"learning":[27,152],"methods":[28],"image,":[31],"audio":[32],"and":[33,117,121,135,154,179],"text":[34],"data,":[35],"significant":[36],"efforts":[37],"recently":[39],"being":[40],"devoted":[41],"design":[44],"nets":[47,93],"for":[48,197],"analytics.":[50],"Among":[51],"many":[53,71,142],"practical":[54,140],"needs,":[55],"classifying":[56],"videos":[57],"(or":[58],"clips)":[60],"based":[61],"on":[62,94,101,160,168],"their":[63],"major":[64],"categories":[66],"(e.g.,\"skiing\")":[67],"is":[68],"useful":[69],"applications.":[72],"In":[73,130],"this":[74,182,201],"paper,":[75],"we":[76],"conduct":[77],"an":[78],"in-depth":[79],"study":[80],"investigate":[82],"important":[83,143],"implementation":[84],"options":[85,144],"that":[86,176],"may":[87],"affect":[88],"performance":[90,125],"classification.":[96],"Our":[97],"evaluations":[98],"conducted":[100],"top":[102],"a":[104,139,194],"recent":[105],"two-stream":[106],"convolutional":[107],"neural":[108],"network":[109,148],"(CNN)":[110],"pipeline,":[111],"which":[112],"uses":[113],"both":[114],"static":[115],"frames":[116],"motion":[118],"optical":[119],"flows,":[120],"has":[122],"demonstrated":[123],"competitive":[124,164],"against":[126],"state-of-the-art":[128],"methods.":[129,158],"order":[131],"gain":[133],"insights":[134],"arrive":[137],"at":[138],"guideline,":[141],"studied,":[146],"including":[147],"architectures,":[149],"model":[150],"fusion,":[151],"parameters":[153],"final":[156],"prediction":[157],"Based":[159],"evaluations,":[162],"results":[165],"attained":[167],"two":[169],"popular":[170],"classification":[172],"benchmarks.":[173],"We":[174],"hope":[175],"discussions":[178],"conclusions":[180],"from":[181],"work":[183],"can":[184],"help":[185],"researchers":[186],"related":[188],"fields":[189],"quickly":[191],"set":[192],"up":[193],"good":[195],"basis":[196],"further":[198],"investigations":[199],"along":[200],"promising":[203],"direction.":[204]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":11},{"year":2019,"cited_by_count":15},{"year":2018,"cited_by_count":15},{"year":2017,"cited_by_count":16},{"year":2016,"cited_by_count":9},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}
