{"id":"https://openalex.org/W3153043989","doi":"https://doi.org/10.1109/tmm.2021.3073235","title":"Action Coherence Network for Weakly-Supervised Temporal Action Localization","display_name":"Action Coherence Network for Weakly-Supervised Temporal Action Localization","publication_year":2021,"publication_date":"2021-04-14","ids":{"openalex":"https://openalex.org/W3153043989","doi":"https://doi.org/10.1109/tmm.2021.3073235","mag":"3153043989"},"language":"en","primary_location":{"id":"doi:10.1109/tmm.2021.3073235","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2021.3073235","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"is_oa":false,"is_in_doaj":false,"is_core":true,"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":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Multimedia","raw_type":"journal-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/A5024925731","display_name":"Yuanhao Zhai","orcid":"https://orcid.org/0000-0002-3277-3329"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuanhao Zhai","raw_affiliation_strings":["Institute of Artificial Intelligence and Robotics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, Shaanxi, China"],"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence and Robotics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, Shaanxi, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100350774","display_name":"Le Wang","orcid":"https://orcid.org/0000-0001-6636-6396"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Le Wang","raw_affiliation_strings":["Institute of Artificial Intelligence and Robotics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, Shaanxi, China"],"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence and Robotics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, Shaanxi, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009121711","display_name":"Wei Tang","orcid":"https://orcid.org/0000-0001-8879-8325"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Tang","raw_affiliation_strings":["Department of Computer Science, University of Illinois, Chicago, IL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Illinois, Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100756983","display_name":"Qilin Zhang","orcid":"https://orcid.org/0000-0002-7917-9749"},"institutions":[{"id":"https://openalex.org/I2948539688","display_name":"AbbVie (United States)","ror":"https://ror.org/02g5p4n58","country_code":"US","type":"company","lineage":["https://openalex.org/I2948539688"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qilin Zhang","raw_affiliation_strings":["ABB Corporate Research Center, Raleigh, NC, USA"],"affiliations":[{"raw_affiliation_string":"ABB Corporate Research Center, Raleigh, NC, USA","institution_ids":["https://openalex.org/I2948539688"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047405956","display_name":"Nanning Zheng","orcid":"https://orcid.org/0000-0003-1608-8257"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nanning Zheng","raw_affiliation_strings":["Institute of Artificial Intelligence and Robotics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, Shaanxi, China"],"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence and Robotics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, Shaanxi, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081114810","display_name":"Gang Hua","orcid":"https://orcid.org/0000-0001-9522-6157"},"institutions":[{"id":"https://openalex.org/I4210108985","display_name":"Bellevue Hospital Center","ror":"https://ror.org/01ky34z31","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1283621791","https://openalex.org/I4210086933","https://openalex.org/I4210108985"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gang Hua","raw_affiliation_strings":["Wormpex AI Research, Bellevue, WA, USA"],"affiliations":[{"raw_affiliation_string":"Wormpex AI Research, Bellevue, WA, USA","institution_ids":["https://openalex.org/I4210108985"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5024925731"],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":1.7292,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.86418301,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":"24","issue":null,"first_page":"1857","last_page":"1870"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9954000115394592,"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/T11227","display_name":"Diabetic Foot Ulcer Assessment and Management","score":0.9902999997138977,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8061339259147644},{"id":"https://openalex.org/keywords/coherence","display_name":"Coherence (philosophical gambling strategy)","score":0.6723722219467163},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6571320295333862},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6189587712287903},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.5794793963432312},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.5555249452590942},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5079556107521057},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.4891248345375061},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.4492718279361725},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34252214431762695},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.20353367924690247},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1398773491382599},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08870181441307068}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8061339259147644},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.6723722219467163},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6571320295333862},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6189587712287903},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.5794793963432312},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.5555249452590942},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5079556107521057},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.4891248345375061},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.4492718279361725},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34252214431762695},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.20353367924690247},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1398773491382599},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08870181441307068},{"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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2021.3073235","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2021.3073235","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"is_oa":false,"is_in_doaj":false,"is_core":true,"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":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Multimedia","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.4300000071525574,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G1534200972","display_name":null,"funder_award_id":"61773312","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3833898155","display_name":null,"funder_award_id":"62088102","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6291262105","display_name":null,"funder_award_id":"61976171","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":75,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W787785461","https://openalex.org/W1522734439","https://openalex.org/W1578985305","https://openalex.org/W1839676122","https://openalex.org/W1923404803","https://openalex.org/W1927052826","https://openalex.org/W2018668305","https://openalex.org/W2020163092","https://openalex.org/W2081899160","https://openalex.org/W2102605133","https://openalex.org/W2105101328","https://openalex.org/W2108598243","https://openalex.org/W2126532873","https://openalex.org/W2131042978","https://openalex.org/W2136853139","https://openalex.org/W2235034809","https://openalex.org/W2342662179","https://openalex.org/W2463824207","https://openalex.org/W2507009361","https://openalex.org/W2514087538","https://openalex.org/W2519328139","https://openalex.org/W2529163075","https://openalex.org/W2593722617","https://openalex.org/W2597958930","https://openalex.org/W2604113307","https://openalex.org/W2607566495","https://openalex.org/W2746923101","https://openalex.org/W2751832138","https://openalex.org/W2755876276","https://openalex.org/W2757143884","https://openalex.org/W2766402183","https://openalex.org/W2884002012","https://openalex.org/W2884293275","https://openalex.org/W2888003147","https://openalex.org/W2893390896","https://openalex.org/W2895240652","https://openalex.org/W2902190902","https://openalex.org/W2920582597","https://openalex.org/W2947084868","https://openalex.org/W2948229620","https://openalex.org/W2952435096","https://openalex.org/W2962677524","https://openalex.org/W2962709777","https://openalex.org/W2962876901","https://openalex.org/W2963015194","https://openalex.org/W2963045696","https://openalex.org/W2963247196","https://openalex.org/W2963321993","https://openalex.org/W2963775820","https://openalex.org/W2963901365","https://openalex.org/W2964008341","https://openalex.org/W2964036161","https://openalex.org/W2964107628","https://openalex.org/W2964214371","https://openalex.org/W2964216549","https://openalex.org/W2971032914","https://openalex.org/W2981578854","https://openalex.org/W2983918066","https://openalex.org/W2984478308","https://openalex.org/W2984619425","https://openalex.org/W2986407524","https://openalex.org/W2988098865","https://openalex.org/W2988630963","https://openalex.org/W2998702159","https://openalex.org/W3004505825","https://openalex.org/W3035585099","https://openalex.org/W3100481960","https://openalex.org/W3109715102","https://openalex.org/W3173874725","https://openalex.org/W3176853098","https://openalex.org/W4250482878","https://openalex.org/W4301963599","https://openalex.org/W6622789128","https://openalex.org/W6682864246"],"related_works":["https://openalex.org/W4360995138","https://openalex.org/W2902032977","https://openalex.org/W290554818","https://openalex.org/W2940661641","https://openalex.org/W3172812035","https://openalex.org/W2621092033","https://openalex.org/W3112881166","https://openalex.org/W4280645146","https://openalex.org/W4205427266","https://openalex.org/W2523244957"],"abstract_inverted_index":{"Weakly-supervised":[0],"Temporal":[1],"Action":[2,71],"Localization":[3],"(W-TAL)":[4],"aims":[5],"at":[6],"simultaneously":[7],"classifying":[8],"and":[9,37,43,55,92,101,124,165,176,188],"locating":[10],"all":[11],"action":[12,35,45,98,107,140,150,169],"instances":[13],"with":[14],"only":[15],"video-level":[16],"supervision.":[17],"However,":[18],"current":[19],"W-TAL":[20,186],"methods":[21],"have":[22],"two":[23,64,129,154],"limitations.":[24],"First,":[25],"they":[26],"ignore":[27],"the":[28,48,52,111,118,148,167,174,184,207,211],"difference":[29],"in":[30,77],"video":[31,93],"representations":[32,95],"between":[33],"an":[34,70],"instance":[36],"its":[38],"surrounding":[39],"background":[40],"when":[41,61],"generating":[42],"scoring":[44],"proposals.":[46],"Second,":[47],"unique":[49],"characteristics":[50,120],"of":[51,121,133,147,204],"RGB":[53,122],"frames":[54,123],"optical":[56,125],"flow":[57,126],"are":[58],"largely":[59],"ignored":[60],"fusing":[62],"these":[63,68],"modalities.":[65],"To":[66],"address":[67],"problems,":[69],"Coherence":[72],"Network":[73],"(ACN)":[74],"is":[75,82,135,160,189],"proposed":[76,112],"this":[78],"paper.":[79],"Its":[80],"core":[81],"a":[83,156,200],"new":[84],"coherence":[85],"loss":[86],"which":[87,134],"exploits":[88],"both":[89],"classification":[90],"predictions":[91],"content":[94],"to":[96,104,137,144,162,192],"supervise":[97],"boundary":[99],"regression":[100],"thus":[102],"leads":[103],"more":[105],"accurate":[106],"localization":[108,170],"results.":[109,171],"Besides,":[110],"ACN":[113,182,198],"explicitly":[114],"takes":[115],"into":[116],"account":[117],"specific":[119],"by":[127,153],"training":[128],"separate":[130],"sub-networks,":[131],"each":[132],"able":[136],"generate":[138],"modality-specific":[139],"proposals":[141,151],"independently.":[142],"Finally,":[143],"take":[145],"advantage":[146],"complementary":[149],"generated":[152],"streams,":[155],"novel":[157],"fusion":[158],"module":[159],"introduced":[161],"reconcile":[163],"them":[164],"obtain":[166],"final":[168],"Experiments":[172],"on":[173,206],"THUMOS14":[175,208],"ActivityNet":[177],"datasets":[178],"show":[179],"that":[180],"our":[181],"outperforms":[183],"state-of-the-art":[185],"methods,":[187],"even":[190],"comparable":[191],"some":[193],"recent":[194],"fully-supervised":[195],"methods.":[196],"Particularly,":[197],"achieves":[199],"mean":[201],"average":[202],"precision":[203],"26.4%":[205],"dataset":[209],"under":[210],"IoU":[212],"threshold":[213],"0.5.":[214]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
