{"id":"https://openalex.org/W4293519281","doi":"https://doi.org/10.1109/icme52920.2022.9859752","title":"GLTA-GCN: Global-Local Temporal Attention Graph Convolutional Network for Unsupervised Skeleton-Based Action Recognition","display_name":"GLTA-GCN: Global-Local Temporal Attention Graph Convolutional Network for Unsupervised Skeleton-Based Action Recognition","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4293519281","doi":"https://doi.org/10.1109/icme52920.2022.9859752"},"language":"en","primary_location":{"id":"doi:10.1109/icme52920.2022.9859752","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme52920.2022.9859752","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":"conference-paper","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/A5076390734","display_name":"Haoyue Qiu","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoyue Qiu","raw_affiliation_strings":["School of Computer Science, Fudan University,Shanghai,China","School of Computer Science, Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University,Shanghai,China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082844557","display_name":"Yuan Wu","orcid":"https://orcid.org/0000-0001-6661-9461"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Wu","raw_affiliation_strings":["School of Computer Science, Fudan University,Shanghai,China","School of Computer Science, Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University,Shanghai,China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091420019","display_name":"Mengmeng Duan","orcid":"https://orcid.org/0000-0002-0753-6663"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"MengMeng Duan","raw_affiliation_strings":["School of Computer Science, Fudan University,Shanghai,China","School of Computer Science, Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University,Shanghai,China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100625873","display_name":"Cheng Jin","orcid":"https://orcid.org/0000-0003-3063-1957"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Jin","raw_affiliation_strings":["School of Computer Science, Fudan University,Shanghai,China","Peng Cheng Laboratory, Shenzhen, China","School of Computer Science, Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University,Shanghai,China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.5718,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.89398993,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9919000267982483,"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/graph","display_name":"Graph","score":0.6677020788192749},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6635661125183105},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5871559381484985},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5847582221031189},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5366214513778687},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.5298642516136169},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5072419047355652},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.44783639907836914},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.44126173853874207},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.41654863953590393},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.370670884847641},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1914007067680359}],"concepts":[{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6677020788192749},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6635661125183105},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5871559381484985},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5847582221031189},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5366214513778687},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.5298642516136169},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5072419047355652},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.44783639907836914},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.44126173853874207},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.41654863953590393},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.370670884847641},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1914007067680359},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme52920.2022.9859752","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme52920.2022.9859752","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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5374055679","display_name":null,"funder_award_id":"62176064,61732004","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":23,"referenced_works":["https://openalex.org/W1950788856","https://openalex.org/W2048821851","https://openalex.org/W2604321021","https://openalex.org/W2787919227","https://openalex.org/W2944006115","https://openalex.org/W2948058585","https://openalex.org/W2948246283","https://openalex.org/W2950568498","https://openalex.org/W2963076818","https://openalex.org/W2964134613","https://openalex.org/W3034548564","https://openalex.org/W3035050855","https://openalex.org/W3035225512","https://openalex.org/W3156509901","https://openalex.org/W3170039146","https://openalex.org/W3171340595","https://openalex.org/W3186755040","https://openalex.org/W3206873236","https://openalex.org/W4385245566","https://openalex.org/W6640754710","https://openalex.org/W6739901393","https://openalex.org/W6797034181","https://openalex.org/W6802722521"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W2067317451","https://openalex.org/W2154771632","https://openalex.org/W4211085505","https://openalex.org/W3122478268","https://openalex.org/W2084758217","https://openalex.org/W2964954556","https://openalex.org/W4303411729","https://openalex.org/W2965803933","https://openalex.org/W4211202157"],"abstract_inverted_index":{"Unsupervised":[0],"skeleton-based":[1],"action":[2],"recognition":[3],"has":[4],"attracted":[5],"increasing":[6],"attention.":[7],"Existing":[8],"methods":[9,28],"have":[10],"several":[11],"limitations:":[12],"(1)":[13],"Many":[14],"actions":[15],"are":[16,102],"highly":[17],"related":[18],"to":[19,69,93,104,108],"local":[20,80,112],"joints,":[21],"which":[22],"is":[23,48,67,130],"often":[24],"neglected.":[25],"(2)":[26],"Most":[27],"directly":[29],"employ":[30],"joint":[31,113],"coordinates":[32],"as":[33],"frame":[34],"feature":[35,114],"and":[36,81,90,115],"do":[37],"not":[38,49],"utilize":[39],"skeleton":[40],"graph,":[41],"e.g.,":[42],"topological":[43],"information.":[44,119],"(3)":[45],"Long-range":[46],"dependency":[47],"captured":[50],"well.":[51],"In":[52],"this":[53],"work,":[54],"a":[55],"novel":[56],"unsupervised":[57],"method":[58],"called":[59],"Global-Local":[60],"Temporal":[61],"Attention":[62],"Graph":[63],"Convolutional":[64],"Network":[65],"(GLTA-GCN)":[66],"proposed":[68],"alleviate":[70],"the":[71,106],"above":[72],"problems.":[73],"The":[74],"network":[75],"consists":[76],"of":[77],"two":[78,99],"branches,":[79],"global":[82],"branches.":[83],"Each":[84],"one":[85],"utilizes":[86],"graph":[87],"convolution":[88],"units":[89],"self-attention":[91],"mechanism":[92],"better":[94],"extract":[95,109],"spatio-temporal":[96],"features.":[97],"Furthermore,":[98],"loss":[100],"functions":[101],"designed":[103],"constrain":[105],"model":[107],"more":[110],"essential":[111],"maintain":[116],"intrinsic":[117],"structural":[118],"Extensive":[120],"experiments":[121],"demonstrate":[122],"that":[123],"GLTA-GCN":[124],"achieves":[125],"state-of-the-art":[126],"performance.":[127],"Our":[128],"code":[129],"released":[131],"on":[132],"https://github.com/HaoyueQiu/GLTA-GCN.":[133]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
