{"id":"https://openalex.org/W2954518547","doi":"https://doi.org/10.1145/3337722.3337755","title":"Making CNNs for video parsing accessible","display_name":"Making CNNs for video parsing accessible","publication_year":2019,"publication_date":"2019-08-26","ids":{"openalex":"https://openalex.org/W2954518547","doi":"https://doi.org/10.1145/3337722.3337755","mag":"2954518547"},"language":"en","primary_location":{"id":"doi:10.1145/3337722.3337755","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3337722.3337755","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3337722.3337755","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th International Conference on the Foundations of Digital Games","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3337722.3337755","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023968907","display_name":"Zijin Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zijin Luo","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108108604","display_name":"Matthew Guzdial","orcid":null},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthew Guzdial","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061883150","display_name":"Mark Riedl","orcid":"https://orcid.org/0000-0001-5283-6588"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mark Riedl","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5079,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.69011179,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11439","display_name":"Video Analysis and Summarization","score":0.9988999962806702,"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/T11439","display_name":"Video Analysis and Summarization","score":0.9988999962806702,"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/T11674","display_name":"Sports Analytics and Performance","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.989300012588501,"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":0.8478690385818481},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6923310160636902},{"id":"https://openalex.org/keywords/tournament","display_name":"Tournament","score":0.6341296434402466},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.5797736048698425},{"id":"https://openalex.org/keywords/video-game","display_name":"Video game","score":0.4980194568634033},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47539815306663513},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43951523303985596},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4277636408805847},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.409238338470459},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.37244367599487305},{"id":"https://openalex.org/keywords/multimedia","display_name":"Multimedia","score":0.3498613238334656}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8478690385818481},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6923310160636902},{"id":"https://openalex.org/C136975688","wikidata":"https://www.wikidata.org/wiki/Q1320634","display_name":"Tournament","level":2,"score":0.6341296434402466},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.5797736048698425},{"id":"https://openalex.org/C3018412434","wikidata":"https://www.wikidata.org/wiki/Q7889","display_name":"Video game","level":2,"score":0.4980194568634033},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47539815306663513},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43951523303985596},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4277636408805847},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.409238338470459},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.37244367599487305},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.3498613238334656},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3337722.3337755","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3337722.3337755","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3337722.3337755","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th International Conference on the Foundations of Digital Games","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1906.11877","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1906.11877","pdf_url":"https://arxiv.org/pdf/1906.11877","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":"doi:10.1145/3337722.3337755","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3337722.3337755","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3337722.3337755","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th International Conference on the Foundations of Digital Games","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1041884219","display_name":null,"funder_award_id":"IIS-1525967","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3037942215","display_name":"CHS: Small: Scientific Design of Interactive Human Computation Systems","funder_award_id":"1525967","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2954518547.pdf","grobid_xml":"https://content.openalex.org/works/W2954518547.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W398859631","https://openalex.org/W652269744","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W1934019294","https://openalex.org/W2025371516","https://openalex.org/W2086598255","https://openalex.org/W2086661052","https://openalex.org/W2108598243","https://openalex.org/W2145085734","https://openalex.org/W2146772569","https://openalex.org/W2194775991","https://openalex.org/W2272300165","https://openalex.org/W2284050935","https://openalex.org/W2338918436","https://openalex.org/W2400717490","https://openalex.org/W2475287302","https://openalex.org/W2507699225","https://openalex.org/W2529250118","https://openalex.org/W2591694232","https://openalex.org/W2726805909","https://openalex.org/W2753866434","https://openalex.org/W2786877691","https://openalex.org/W2803023299","https://openalex.org/W2886187052","https://openalex.org/W2891250075","https://openalex.org/W2897852577","https://openalex.org/W2949117887","https://openalex.org/W2949710607","https://openalex.org/W2962851801","https://openalex.org/W2963685250","https://openalex.org/W2964233199","https://openalex.org/W2964299589","https://openalex.org/W4297813615","https://openalex.org/W4312626470","https://openalex.org/W4313047384"],"related_works":["https://openalex.org/W3123786980","https://openalex.org/W2994965880","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"The":[0],"ability":[1],"to":[2,16,25,35,39,63,77,91,99,106,118,125,148],"extract":[3,107],"sequences":[4],"of":[5,116,134,137,162,165],"game":[6,150],"events":[7,108],"for":[8],"high-resolution":[9],"e-sport":[10,69,110],"games":[11],"has":[12],"traditionally":[13],"required":[14,94],"access":[15,62,147],"the":[17,93,132,157,160,166],"game's":[18],"engine.":[19],"This":[20,113,130],"serves":[21,52],"as":[22,53,67],"a":[23,88,101,120],"barrier":[24],"groups":[26,58],"who":[27,72],"don't":[28],"possess":[29],"this":[30,84,153],"access.":[31],"It":[32],"is":[33],"possible":[34],"apply":[36,100],"deep":[37],"learning":[38],"derive":[40],"these":[41,64,141],"logs":[42,151],"from":[43,61,109],"gameplay":[44,76,111],"video,":[45],"but":[46],"it":[47],"requires":[48],"computational":[49,95],"power":[50],"that":[51],"an":[54],"additional":[55],"barrier.":[56],"These":[57],"would":[59],"benefit":[60],"logs,":[65],"such":[66],"small":[68],"tournament":[70],"organizers":[71],"could":[73],"better":[74],"visualize":[75],"inform":[78],"both":[79],"audience":[80],"and":[81,97,123,139],"commentators.":[82],"In":[83],"paper":[85],"we":[86],"present":[87],"combined":[89],"solution":[90,114],"reduce":[92],"resources":[96],"time":[98],"convolutional":[102],"neural":[103],"network":[104],"(CNN)":[105],"videos.":[112],"consists":[115],"techniques":[117],"train":[119],"CNN":[121],"faster":[122],"methods":[124],"execute":[126],"predictions":[127],"more":[128],"quickly.":[129],"expands":[131],"types":[133],"machines":[135],"capable":[136],"training":[138],"running":[140],"models,":[142],"which":[143],"in":[144,159],"turn":[145],"extends":[146],"extracting":[149],"with":[152],"approach.":[154],"We":[155],"evaluate":[156],"approaches":[158],"domain":[161],"DOTA2,":[163],"one":[164],"most":[167],"popular":[168],"e-sports.":[169],"Our":[170],"results":[171],"demonstrate":[172],"our":[173],"approach":[174],"outperforms":[175],"standard":[176],"backpropagation":[177],"baselines.":[178]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-22T08:00:12.763002","created_date":"2025-10-10T00:00:00"}
