{"id":"https://openalex.org/W4207056973","doi":"https://doi.org/10.1109/tale52509.2021.9678609","title":"Two-Stream Completeness Modeling for Weakly Supervised Temporal Action Detection","display_name":"Two-Stream Completeness Modeling for Weakly Supervised Temporal Action Detection","publication_year":2021,"publication_date":"2021-12-05","ids":{"openalex":"https://openalex.org/W4207056973","doi":"https://doi.org/10.1109/tale52509.2021.9678609"},"language":"en","primary_location":{"id":"doi:10.1109/tale52509.2021.9678609","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tale52509.2021.9678609","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Engineering, Technology & Education (TALE)","raw_type":"proceedings-article"},"type":"article","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/A5084224031","display_name":"Xiaoqiu Chen","orcid":"https://orcid.org/0000-0002-3012-7091"},"institutions":[{"id":"https://openalex.org/I88830068","display_name":"Shaanxi Normal University","ror":"https://ror.org/0170z8493","country_code":"CN","type":"education","lineage":["https://openalex.org/I88830068"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaoqiu Chen","raw_affiliation_strings":["School of Computer Science, Shaanxi Normal University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Shaanxi Normal University, Xi'an, China","institution_ids":["https://openalex.org/I88830068"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020606089","display_name":"Mengge Li","orcid":"https://orcid.org/0000-0002-7968-2643"},"institutions":[{"id":"https://openalex.org/I88830068","display_name":"Shaanxi Normal University","ror":"https://ror.org/0170z8493","country_code":"CN","type":"education","lineage":["https://openalex.org/I88830068"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengge Li","raw_affiliation_strings":["School of Computer Science, Shaanxi Normal University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Shaanxi Normal University, Xi'an, China","institution_ids":["https://openalex.org/I88830068"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026771368","display_name":"Miao Ma","orcid":"https://orcid.org/0000-0001-7735-6695"},"institutions":[{"id":"https://openalex.org/I88830068","display_name":"Shaanxi Normal University","ror":"https://ror.org/0170z8493","country_code":"CN","type":"education","lineage":["https://openalex.org/I88830068"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Miao Ma","raw_affiliation_strings":["School of Computer Science, Shaanxi Normal University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Shaanxi Normal University, Xi'an, China","institution_ids":["https://openalex.org/I88830068"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5084224031"],"corresponding_institution_ids":["https://openalex.org/I88830068"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18338235,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"823","last_page":"828"},"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/T11227","display_name":"Diabetic Foot Ulcer Assessment and Management","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9884999990463257,"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/computer-science","display_name":"Computer science","score":0.7799336910247803},{"id":"https://openalex.org/keywords/completeness","display_name":"Completeness (order theory)","score":0.7204721570014954},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.718342661857605},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5376570820808411},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45955100655555725},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.45682576298713684},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4193386137485504},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3549214005470276},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3535943031311035},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1650124192237854},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10265344381332397}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7799336910247803},{"id":"https://openalex.org/C17231256","wikidata":"https://www.wikidata.org/wiki/Q5156540","display_name":"Completeness (order theory)","level":2,"score":0.7204721570014954},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.718342661857605},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5376570820808411},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45955100655555725},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.45682576298713684},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4193386137485504},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3549214005470276},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3535943031311035},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1650124192237854},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10265344381332397},{"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tale52509.2021.9678609","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tale52509.2021.9678609","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Engineering, Technology & Education (TALE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7367589139","display_name":null,"funder_award_id":"61877038","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1927052826","https://openalex.org/W2156303437","https://openalex.org/W2336403884","https://openalex.org/W2507009361","https://openalex.org/W2593722617","https://openalex.org/W2604113307","https://openalex.org/W2751445731","https://openalex.org/W2884293275","https://openalex.org/W2895240652","https://openalex.org/W2920182456","https://openalex.org/W2948229620","https://openalex.org/W2952435096","https://openalex.org/W2962709777","https://openalex.org/W2963247196","https://openalex.org/W2963524571","https://openalex.org/W2963731667","https://openalex.org/W2964214371","https://openalex.org/W2964216549","https://openalex.org/W2988098865","https://openalex.org/W2998702159","https://openalex.org/W3109986575","https://openalex.org/W4301963599","https://openalex.org/W6682864246","https://openalex.org/W6760239191"],"related_works":["https://openalex.org/W4237277701","https://openalex.org/W2121424666","https://openalex.org/W1587028174","https://openalex.org/W4301250310","https://openalex.org/W2030928163","https://openalex.org/W4285328440","https://openalex.org/W4390062853","https://openalex.org/W4389256085","https://openalex.org/W4399290976","https://openalex.org/W4313644201"],"abstract_inverted_index":{"In":[0,141],"the":[1,6,43,71,90,97,100,107,110,136,160,172,192,196,206],"process":[2],"of":[3,42,96,99,109,129,162,186],"dataset":[4,150,194],"construction,":[5],"cost":[7],"is":[8,132,153,178],"expensive":[9],"to":[10,92,114,134,158,180],"make":[11,93],"frame-wise":[12],"temporal":[13,20,86,147],"annotations":[14,30],"for":[15],"videos.":[16],"Therefore,":[17,63],"weakly":[18,44],"supervised":[19,45],"action":[21,28,57,118,148],"detection":[22,149,203],"methods":[23,46,183],"which":[24,152],"only":[25,177],"leverage":[26],"video-level":[27],"categories":[29],"during":[31],"training":[32],"have":[33,52],"become":[34],"an":[35],"important":[36],"research":[37],"branch.":[38],"At":[39],"present,":[40],"most":[41,181],"adopt":[47],"feature":[48],"early":[49],"fusion,":[50],"and":[51,59,85,195],"two":[53,101],"inevitable":[54],"problems,":[55],"namely":[56],"completeness":[58,113],"background":[60,139],"frame":[61],"interference.":[62],"this":[64,78,143],"paper":[65,144],"proposes":[66],"a":[67,127,146],"method":[68,79,164,175],"based":[69,154],"on":[70,155,191,205],"Two-Stream":[72],"Completeness":[73],"Modeling":[74],"(TSCM)":[75],"network.":[76],"First,":[77],"separately":[80],"inputs":[81],"spatial":[82],"flow":[83,87],"features":[84,88],"into":[89],"network":[91],"full":[94],"use":[95],"characteristics":[98],"modal":[102],"features.":[103],"Second,":[104],"it":[105],"employs":[106],"modeling":[108],"multibranch":[111],"complementary":[112],"generate":[115],"as":[116,120],"complete":[117],"instances":[119],"possible.":[121],"Finally,":[122],"Angular":[123],"Center":[124],"Loss":[125],"with":[126],"Pair":[128],"Triplets":[130],"(ACL-PT)":[131],"designed":[133],"suppress":[135],"interference":[137],"from":[138],"frames.":[140],"particular,":[142],"constructs":[145],"(STAD),":[151],"learning":[156],"scenes":[157],"explore":[159],"effectiveness":[161],"our":[163],"in":[165,184],"real":[166],"applications.":[167],"Experimental":[168],"results":[169],"show":[170],"that":[171],"proposed":[173],"TSCM":[174],"not":[176],"superior":[179],"mainstream":[182],"terms":[185],"mean":[187],"Average":[188],"Precision":[189],"(mAP)":[190],"THUMOS14":[193],"ActivityNet1.2":[197],"dataset,":[198],"but":[199],"also":[200],"achieves":[201],"good":[202],"accuracy":[204],"STAD":[207],"dataset.":[208]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}