{"id":"https://openalex.org/W3155314650","doi":"https://doi.org/10.1145/3514221.3526181","title":"Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning","display_name":"Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning","publication_year":2022,"publication_date":"2022-06-10","ids":{"openalex":"https://openalex.org/W3155314650","doi":"https://doi.org/10.1145/3514221.3526181","mag":"3155314650"},"language":"en","primary_location":{"id":"doi:10.1145/3514221.3526181","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3514221.3526181","pdf_url":null,"source":{"id":"https://openalex.org/S4363608845","display_name":"Proceedings of the 2022 International Conference on Management of Data","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 International Conference on Management of Data","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1145/3514221.3526181","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079091780","display_name":"Pramod Chunduri","orcid":"https://orcid.org/0000-0001-5163-6234"},"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":true,"raw_author_name":"Pramod Chunduri","raw_affiliation_strings":["Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077654785","display_name":"Jaeho Bang","orcid":"https://orcid.org/0000-0002-1656-3372"},"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":"Jaeho Bang","raw_affiliation_strings":["Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101882874","display_name":"Yao Lu","orcid":"https://orcid.org/0000-0002-3043-6185"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yao Lu","raw_affiliation_strings":["Microsoft Research, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060680349","display_name":"Joy Arulraj","orcid":"https://orcid.org/0000-0002-7706-6978"},"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":"Joy Arulraj","raw_affiliation_strings":["Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5079091780"],"corresponding_institution_ids":["https://openalex.org/I130701444"],"apc_list":null,"apc_paid":null,"fwci":0.5384,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.73967353,"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":"545","last_page":"558"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":1.0,"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":1.0,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9994999766349792,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9991000294685364,"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/zeus","display_name":"ZEUS (particle detector)","score":0.8227670192718506},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7161633372306824},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6318982839584351},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5239971876144409},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39982491731643677},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.33208316564559937}],"concepts":[{"id":"https://openalex.org/C2776444479","wikidata":"https://www.wikidata.org/wiki/Q8063038","display_name":"ZEUS (particle detector)","level":5,"score":0.8227670192718506},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7161633372306824},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6318982839584351},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5239971876144409},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39982491731643677},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.33208316564559937},{"id":"https://openalex.org/C142199849","wikidata":"https://www.wikidata.org/wiki/Q3027672","display_name":"Inelastic scattering","level":3,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"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/C89473665","wikidata":"https://www.wikidata.org/wiki/Q2748917","display_name":"Deep inelastic scattering","level":4,"score":0.0},{"id":"https://openalex.org/C191486275","wikidata":"https://www.wikidata.org/wiki/Q210028","display_name":"Scattering","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3514221.3526181","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3514221.3526181","pdf_url":null,"source":{"id":"https://openalex.org/S4363608845","display_name":"Proceedings of the 2022 International Conference on Management of Data","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 International Conference on Management of Data","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2104.06142","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2104.06142","pdf_url":"https://arxiv.org/pdf/2104.06142","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:10.1145/3514221.3526181","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3514221.3526181","pdf_url":null,"source":{"id":"https://openalex.org/S4363608845","display_name":"Proceedings of the 2022 International Conference on Management of Data","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 International Conference on Management of Data","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.5400000214576721,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1757796397","https://openalex.org/W1927052826","https://openalex.org/W1983364832","https://openalex.org/W2016053056","https://openalex.org/W2028145673","https://openalex.org/W2103561211","https://openalex.org/W2115579991","https://openalex.org/W2124386111","https://openalex.org/W2161969291","https://openalex.org/W2200545814","https://openalex.org/W2340897893","https://openalex.org/W2342662179","https://openalex.org/W2752236330","https://openalex.org/W2764754194","https://openalex.org/W2769041395","https://openalex.org/W2781922022","https://openalex.org/W2786278116","https://openalex.org/W2798554470","https://openalex.org/W2948513753","https://openalex.org/W2952186347","https://openalex.org/W2962876901","https://openalex.org/W2963155035","https://openalex.org/W2963322247","https://openalex.org/W2963524571","https://openalex.org/W2964121744","https://openalex.org/W2964214371","https://openalex.org/W2970851599","https://openalex.org/W2992371683","https://openalex.org/W3000318171","https://openalex.org/W3015101615","https://openalex.org/W3015722773","https://openalex.org/W3028942915","https://openalex.org/W3030888939","https://openalex.org/W3035564946","https://openalex.org/W3037207827","https://openalex.org/W3081751010","https://openalex.org/W3085193988"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Detection":[0],"and":[1,21,36,45],"localization":[2],"of":[3,42,63],"actions":[4,28],"in":[5,11],"videos":[6],"is":[7,55],"an":[8],"important":[9,57],"problem":[10],"practice.":[12],"State-of-the-art":[13],"video":[14],"analytics":[15],"systems":[16],"are":[17,37],"unable":[18],"to":[19,58,65],"efficiently":[20],"effectively":[22],"answer":[23,66],"such":[24],"action":[25],"queries":[26],"because":[27],"often":[29],"involve":[30],"a":[31,40],"complex":[32],"interaction":[33],"between":[34],"objects":[35],"spread":[38],"across":[39],"sequence":[41,62],"frames;":[43],"detecting":[44],"localizing":[46],"them":[47],"requires":[48],"computationally":[49],"expensive":[50],"deep":[51],"neural":[52],"networks.":[53],"It":[54],"also":[56],"consider":[59],"the":[60,67],"entire":[61],"frames":[64],"query":[68],"effectively.":[69]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
