{"id":"https://openalex.org/W4283834781","doi":"https://doi.org/10.1109/icme52920.2022.9859626","title":"Turning to a Teacher for Timestamp Supervised Temporal Action Segmentation","display_name":"Turning to a Teacher for Timestamp Supervised Temporal Action Segmentation","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4283834781","doi":"https://doi.org/10.1109/icme52920.2022.9859626"},"language":"en","primary_location":{"id":"doi:10.1109/icme52920.2022.9859626","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme52920.2022.9859626","pdf_url":null,"source":{"id":"https://openalex.org/S4363607799","display_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2207.00712","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100362748","display_name":"Yang Zhao","orcid":"https://orcid.org/0000-0003-0843-8202"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Zhao","raw_affiliation_strings":["Nanjing University of Science and Technology,China","Nanjing University of Science and Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology,China","institution_ids":["https://openalex.org/I36399199"]},{"raw_affiliation_string":"Nanjing University of Science and Technology, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055295169","display_name":"Yan Song","orcid":"https://orcid.org/0000-0001-8431-7037"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Song","raw_affiliation_strings":["Nanjing University of Science and Technology,China","Nanjing University of Science and Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology,China","institution_ids":["https://openalex.org/I36399199"]},{"raw_affiliation_string":"Nanjing University of Science and Technology, China","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2949,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.62163697,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"01","last_page":"06"},"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9959999918937683,"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":0.9936000108718872,"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/timestamp","display_name":"Timestamp","score":0.9077790975570679},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8199872970581055},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7821047306060791},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.6345242857933044},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6053329706192017},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5426434278488159},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.529945969581604},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.5055797100067139},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47520360350608826},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.45936837792396545},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33597397804260254},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3316689431667328},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.29107433557510376},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.10022017359733582},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.09514433145523071}],"concepts":[{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.9077790975570679},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8199872970581055},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7821047306060791},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.6345242857933044},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6053329706192017},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5426434278488159},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.529945969581604},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5055797100067139},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47520360350608826},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.45936837792396545},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33597397804260254},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3316689431667328},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.29107433557510376},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.10022017359733582},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.09514433145523071},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icme52920.2022.9859626","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme52920.2022.9859626","pdf_url":null,"source":{"id":"https://openalex.org/S4363607799","display_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2207.00712","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2207.00712","pdf_url":"https://arxiv.org/pdf/2207.00712","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:2207.00712","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2207.00712","pdf_url":"https://arxiv.org/pdf/2207.00712","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":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1988650444","https://openalex.org/W2031688197","https://openalex.org/W2099614498","https://openalex.org/W2109698606","https://openalex.org/W2491875666","https://openalex.org/W2550143307","https://openalex.org/W2798345491","https://openalex.org/W2918597803","https://openalex.org/W2953070460","https://openalex.org/W2962916463","https://openalex.org/W2963524571","https://openalex.org/W2964311439","https://openalex.org/W3008576587","https://openalex.org/W3009622574","https://openalex.org/W3035557275","https://openalex.org/W3083550439","https://openalex.org/W3108772932","https://openalex.org/W3119038403","https://openalex.org/W3180945712","https://openalex.org/W3197164375","https://openalex.org/W6676456542","https://openalex.org/W6749682228","https://openalex.org/W6750913434","https://openalex.org/W6786866944"],"related_works":["https://openalex.org/W2060561905","https://openalex.org/W1417711376","https://openalex.org/W1986883493","https://openalex.org/W2469862403","https://openalex.org/W2166378262","https://openalex.org/W2035891203","https://openalex.org/W4379524643","https://openalex.org/W2011027677","https://openalex.org/W2367807705","https://openalex.org/W4285233590"],"abstract_inverted_index":{"Temporal":[0],"action":[1,76,146],"segmentation":[2,41,77,86,107],"in":[3],"videos":[4],"has":[5],"drawn":[6],"much":[7,170],"attention":[8],"recently.":[9],"Timestamp":[10],"supervision":[11],"is":[12,131],"a":[13,40,69,80,126,169],"cost-effective":[14],"way":[15],"for":[16,72],"this":[17,48,65],"task.":[18],"To":[19,63],"obtain":[20],"more":[21,132],"information":[22],"to":[23,60,84,88,111,116,136],"optimize":[24],"the":[25,27,37,44,56,85,91,106,113,118,138,142,158,165],"model,":[26,108],"existing":[28],"method":[29,156,160],"generated":[30],"pseudo":[31,121],"frame-wise":[32],"labels":[33],"iteratively":[34],"based":[35],"on":[36,150],"output":[38],"of":[39,93,105,120,141],"model":[42,82,87,94,98],"and":[43,53,58,115,134,161],"timestamp":[45,73],"annotations.":[46],"However,":[47],"practice":[49],"may":[50],"introduce":[51,125],"noise":[52,114],"oscillation":[54],"during":[55],"training,":[57],"lead":[59],"performance":[61],"degeneration.":[62],"address":[64],"problem,":[66],"we":[67],"propose":[68],"new":[70],"framework":[71],"supervised":[74],"temporal":[75],"by":[78],"introducing":[79],"teacher":[81,97],"parallel":[83],"help":[89],"stabilize":[90],"process":[92],"optimization.":[95],"The":[96,148],"can":[99],"be":[100],"seen":[101],"as":[102],"an":[103],"ensemble":[104],"which":[109,130],"helps":[110],"suppress":[112],"improve":[117],"stability":[119],"labels.":[122],"We":[123],"further":[124],"segmentally":[127],"smoothing":[128],"loss,":[129],"focused":[133],"cohesive,":[135],"enforce":[137],"smooth":[139],"transition":[140],"predicted":[143],"probabilities":[144],"within":[145],"instances.":[147],"experiments":[149],"three":[151],"datasets":[152],"show":[153],"that":[154],"our":[155],"outperforms":[157],"state-of-the-art":[159],"performs":[162],"comparably":[163],"against":[164],"fully-supervised":[166],"methods":[167],"at":[168],"lower":[171],"annotation":[172],"cost.":[173]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2022-07-07T00:00:00"}
