{"id":"https://openalex.org/W4312775149","doi":"https://doi.org/10.1109/icpr56361.2022.9956432","title":"Efficient Action Recognition Using Confidence Distillation","display_name":"Efficient Action Recognition Using Confidence Distillation","publication_year":2022,"publication_date":"2022-08-21","ids":{"openalex":"https://openalex.org/W4312775149","doi":"https://doi.org/10.1109/icpr56361.2022.9956432"},"language":"en","primary_location":{"id":"doi:10.1109/icpr56361.2022.9956432","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956432","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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 26th International Conference on Pattern Recognition (ICPR)","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/A5088032779","display_name":"Shervin Manzuri Shalmani","orcid":null},"institutions":[{"id":"https://openalex.org/I98251732","display_name":"McMaster University","ror":"https://ror.org/02fa3aq29","country_code":"CA","type":"education","lineage":["https://openalex.org/I98251732"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Shervin Manzuri Shalmani","raw_affiliation_strings":["McMaster University,Department of Computing and Software,Canada","Department of Computing and Software, McMaster University, Canada"],"affiliations":[{"raw_affiliation_string":"McMaster University,Department of Computing and Software,Canada","institution_ids":["https://openalex.org/I98251732"]},{"raw_affiliation_string":"Department of Computing and Software, McMaster University, Canada","institution_ids":["https://openalex.org/I98251732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061267570","display_name":"Fei Chiang","orcid":"https://orcid.org/0000-0003-4128-8074"},"institutions":[{"id":"https://openalex.org/I98251732","display_name":"McMaster University","ror":"https://ror.org/02fa3aq29","country_code":"CA","type":"education","lineage":["https://openalex.org/I98251732"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Fei Chiang","raw_affiliation_strings":["McMaster University,Department of Computing and Software,Canada","Department of Computing and Software, McMaster University, Canada"],"affiliations":[{"raw_affiliation_string":"McMaster University,Department of Computing and Software,Canada","institution_ids":["https://openalex.org/I98251732"]},{"raw_affiliation_string":"Department of Computing and Software, McMaster University, Canada","institution_ids":["https://openalex.org/I98251732"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056442083","display_name":"Rong Zheng","orcid":"https://orcid.org/0000-0003-4070-075X"},"institutions":[{"id":"https://openalex.org/I98251732","display_name":"McMaster University","ror":"https://ror.org/02fa3aq29","country_code":"CA","type":"education","lineage":["https://openalex.org/I98251732"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Rong Zheng","raw_affiliation_strings":["McMaster University,Department of Computing and Software,Canada","Department of Computing and Software, McMaster University, Canada"],"affiliations":[{"raw_affiliation_string":"McMaster University,Department of Computing and Software,Canada","institution_ids":["https://openalex.org/I98251732"]},{"raw_affiliation_string":"Department of Computing and Software, McMaster University, Canada","institution_ids":["https://openalex.org/I98251732"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5088032779"],"corresponding_institution_ids":["https://openalex.org/I98251732"],"apc_list":null,"apc_paid":null,"fwci":0.5988,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.77189496,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3362","last_page":"3369"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9968000054359436,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9871000051498413,"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":0.807874858379364},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6290069222450256},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6256853938102722},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6011015772819519},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5940631031990051},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.5926493406295776},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.5753636360168457},{"id":"https://openalex.org/keywords/clips","display_name":"CLIPS","score":0.5688899755477905},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.5609802007675171},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.5161932110786438},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4365385174751282},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42680680751800537},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.41180622577667236},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1548927128314972},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06669872999191284}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.807874858379364},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6290069222450256},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6256853938102722},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6011015772819519},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5940631031990051},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.5926493406295776},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.5753636360168457},{"id":"https://openalex.org/C2778739407","wikidata":"https://www.wikidata.org/wiki/Q165372","display_name":"CLIPS","level":2,"score":0.5688899755477905},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.5609802007675171},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.5161932110786438},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4365385174751282},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42680680751800537},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.41180622577667236},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1548927128314972},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06669872999191284},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","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/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr56361.2022.9956432","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956432","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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 26th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6399999856948853,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":95,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W1522734439","https://openalex.org/W1586730761","https://openalex.org/W1686810756","https://openalex.org/W1821462560","https://openalex.org/W1825675169","https://openalex.org/W1923404803","https://openalex.org/W1945616565","https://openalex.org/W1947481528","https://openalex.org/W1995875735","https://openalex.org/W2097117768","https://openalex.org/W2117670920","https://openalex.org/W2156303437","https://openalex.org/W2163605009","https://openalex.org/W2188379175","https://openalex.org/W2194775991","https://openalex.org/W2294370754","https://openalex.org/W2342153227","https://openalex.org/W2462906003","https://openalex.org/W2600383743","https://openalex.org/W2619947201","https://openalex.org/W2625366777","https://openalex.org/W2626967530","https://openalex.org/W2746726611","https://openalex.org/W2786712888","https://openalex.org/W2799176631","https://openalex.org/W2803023299","https://openalex.org/W2809562466","https://openalex.org/W2883780447","https://openalex.org/W2884002012","https://openalex.org/W2884797191","https://openalex.org/W2887051120","https://openalex.org/W2895738954","https://openalex.org/W2913340405","https://openalex.org/W2950025868","https://openalex.org/W2951266961","https://openalex.org/W2961193895","https://openalex.org/W2962934715","https://openalex.org/W2963015194","https://openalex.org/W2963091558","https://openalex.org/W2963155035","https://openalex.org/W2963216700","https://openalex.org/W2963246338","https://openalex.org/W2963321993","https://openalex.org/W2963370182","https://openalex.org/W2963447094","https://openalex.org/W2963524571","https://openalex.org/W2963526497","https://openalex.org/W2963645879","https://openalex.org/W2963722382","https://openalex.org/W2963820951","https://openalex.org/W2963995504","https://openalex.org/W2964094092","https://openalex.org/W2981184799","https://openalex.org/W2981548405","https://openalex.org/W2984287396","https://openalex.org/W2988630963","https://openalex.org/W2990152177","https://openalex.org/W2990503944","https://openalex.org/W2997582214","https://openalex.org/W3004505825","https://openalex.org/W3010010212","https://openalex.org/W3034223443","https://openalex.org/W3034572008","https://openalex.org/W3034658206","https://openalex.org/W3035619757","https://openalex.org/W4297775537","https://openalex.org/W4297797729","https://openalex.org/W6600983433","https://openalex.org/W6631456553","https://openalex.org/W6635099914","https://openalex.org/W6637373629","https://openalex.org/W6638389677","https://openalex.org/W6638523607","https://openalex.org/W6640425456","https://openalex.org/W6677107029","https://openalex.org/W6682864246","https://openalex.org/W6684191040","https://openalex.org/W6697633650","https://openalex.org/W6718836005","https://openalex.org/W6735443497","https://openalex.org/W6737664043","https://openalex.org/W6739651123","https://openalex.org/W6744865055","https://openalex.org/W6748163547","https://openalex.org/W6750542987","https://openalex.org/W6751751081","https://openalex.org/W6752810524","https://openalex.org/W6753767121","https://openalex.org/W6753994894","https://openalex.org/W6754337694","https://openalex.org/W6764214684","https://openalex.org/W6765307894","https://openalex.org/W6769760422","https://openalex.org/W6955071965"],"related_works":["https://openalex.org/W2417253731","https://openalex.org/W2350469024","https://openalex.org/W2491583298","https://openalex.org/W2327827625","https://openalex.org/W2395860100","https://openalex.org/W2036154621","https://openalex.org/W795077857","https://openalex.org/W2938698191","https://openalex.org/W3087274204","https://openalex.org/W3004505825"],"abstract_inverted_index":{"Modern":[0],"neural":[1],"networks":[2],"are":[3,55],"powerful":[4],"predictive":[5],"models.":[6],"However,":[7],"when":[8],"it":[9,95],"comes":[10],"to":[11,82,92,118,124,163],"recognizing":[12],"that":[13,60,152],"they":[14,21],"may":[15],"be":[16,93],"wrong":[17],"about":[18],"their":[19],"predictions,":[20],"perform":[22],"poorly.":[23],"For":[24],"example,":[25],"for":[26,66,129],"one":[27],"of":[28,136],"the":[29,34,72,114,130,134,139],"most":[30],"common":[31],"activation":[32],"functions,":[33],"ReLU":[35],"and":[36,70,101,132,150,165],"its":[37],"variants,":[38],"even":[39],"a":[40,63,103,120],"well-calibrated":[41],"model":[42,122],"can":[43],"produce":[44],"incorrect":[45],"but":[46],"high":[47,104],"confidence":[48,115],"predictions.":[49,86],"Most":[50],"current":[51],"action":[52,147,159],"recognition":[53,99,148,160],"methods":[54],"based":[56],"on":[57,145],"clip-level":[58],"classifiers":[59],"densely":[61],"sample":[62],"given":[64],"video":[65,84],"non-overlapping,":[67],"same-sized":[68],"clips":[69,128],"aggregate":[71],"results":[73],"using":[74],"an":[75],"aggregation":[76],"function":[77],"-":[78,81],"typically":[79],"averaging":[80],"achieve":[83],"level":[85],"While":[87],"this":[88],"approach":[89],"has":[90,102],"shown":[91],"effective,":[94],"is":[96],"sub-optimal":[97],"in":[98,158],"accuracy":[100,161],"computational":[105,166],"overhead.":[106],"To":[107],"mitigate":[108],"both":[109],"these":[110],"issues,":[111],"we":[112],"propose":[113],"distillation":[116],"framework":[117,154],"teach":[119],"student":[121],"how":[123],"select":[125],"less":[126],"ambiguous":[127],"teacher,":[131],"divide":[133],"task":[135],"prediction":[137],"between":[138],"two.":[140],"We":[141],"conduct":[142],"extensive":[143],"experiments":[144],"three":[146],"datasets":[149],"demonstrate":[151],"our":[153],"achieves":[155],"significant":[156],"improvements":[157],"(up":[162],"20%)":[164],"efficiency":[167],"(more":[168],"than":[169],"40%).":[170]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3}],"updated_date":"2026-03-14T08:43:22.919905","created_date":"2025-10-10T00:00:00"}