{"id":"https://openalex.org/W3217030072","doi":"https://doi.org/10.1145/3484274.3484288","title":"Temporal-Aware Graph Convolution Network for Skeleton-based Action Recognition","display_name":"Temporal-Aware Graph Convolution Network for Skeleton-based Action Recognition","publication_year":2021,"publication_date":"2021-08-13","ids":{"openalex":"https://openalex.org/W3217030072","doi":"https://doi.org/10.1145/3484274.3484288","mag":"3217030072"},"language":"en","primary_location":{"id":"doi:10.1145/3484274.3484288","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3484274.3484288","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 4th International Conference on Control and Computer Vision","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/A5051731173","display_name":"Yulai Xie","orcid":"https://orcid.org/0000-0003-0764-6579"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yulai Xie","raw_affiliation_strings":["Hitachi(China)Research&Development Corporation, China"],"affiliations":[{"raw_affiliation_string":"Hitachi(China)Research&Development Corporation, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100354625","display_name":"Yang Zhang","orcid":"https://orcid.org/0000-0002-0523-8478"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang Zhang","raw_affiliation_strings":["Hitachi(China)Research&Development Corporation, China"],"affiliations":[{"raw_affiliation_string":"Hitachi(China)Research&Development Corporation, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100652923","display_name":"Fang Ren","orcid":"https://orcid.org/0000-0002-2251-9220"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fang REN","raw_affiliation_strings":["University of Science and Technology Beijing, China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5051731173"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15947712,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"83","last_page":"90"},"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9801999926567078,"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":0.9747999906539917,"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":0.7245547771453857},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6403931975364685},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6160244345664978},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.597196638584137},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5147519111633301},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5037934184074402},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.4605826735496521},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.353005975484848},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3307339549064636},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1563926637172699}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7245547771453857},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6403931975364685},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6160244345664978},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.597196638584137},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5147519111633301},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5037934184074402},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.4605826735496521},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.353005975484848},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3307339549064636},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1563926637172699},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3484274.3484288","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3484274.3484288","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 4th International Conference on Control and Computer Vision","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2559085405","https://openalex.org/W2560662850","https://openalex.org/W2613570903","https://openalex.org/W2619947201","https://openalex.org/W2798644314","https://openalex.org/W2899771611","https://openalex.org/W2899936784","https://openalex.org/W2940457086","https://openalex.org/W2948058585","https://openalex.org/W2963091558","https://openalex.org/W2964134613","https://openalex.org/W2966210862","https://openalex.org/W6756040250"],"related_works":["https://openalex.org/W2168109476","https://openalex.org/W2486460843","https://openalex.org/W1968121071","https://openalex.org/W2020254986","https://openalex.org/W2004108207","https://openalex.org/W2901551566","https://openalex.org/W4303411729","https://openalex.org/W2965803933","https://openalex.org/W4211202157","https://openalex.org/W4287181611"],"abstract_inverted_index":{"Graph":[0,51],"convolutions":[1],"networks":[2],"(GCN)":[3],"have":[4],"drawn":[5],"attention":[6],"for":[7],"skeleton-based":[8],"action":[9,44],"recognition":[10],"because":[11],"a":[12,23,48,58,77],"skeleton":[13],"with":[14],"joints":[15],"and":[16,124],"bones":[17],"can":[18,109],"be":[19],"naturally":[20],"regarded":[21],"as":[22],"graph":[24,80,88],"structure.":[25],"However,":[26],"the":[27,72,90,93,104,111,136],"existing":[28],"methods":[29],"are":[30],"limited":[31],"in":[32,43],"temporal":[33,41,60,94,106],"sequence":[34],"modeling":[35],"of":[36,114],"human":[37,115],"actions.":[38,116],"To":[39],"consider":[40],"factors":[42],"modeling,":[45],"we":[46,56,75],"present":[47,76],"novel":[49,78],"Temporal-Aware":[50],"Convolution":[52],"Network":[53],"(TA-GCN).":[54],"First,":[55],"design":[57],"causal":[59],"convolution":[61,81],"(CTCN)":[62],"layer":[63,83],"to":[64,71,92,96],"ensure":[65],"no":[66],"impractical":[67],"future":[68],"information":[69],"leakage":[70],"past.":[73],"Second,":[74],"cross-spatial-temporal":[79,99],"(3D-GCN)":[82],"that":[84,127],"extends":[85],"an":[86],"adaptive":[87],"from":[89],"spatial":[91],"domain":[95],"capture":[97],"local":[98],"dependencies":[100],"among":[101],"joints.":[102],"Involving":[103],"two":[105,120,137],"factors,":[107],"TA-GCN":[108],"model":[110],"sequential":[112],"nature":[113],"Experimental":[117],"results":[118],"on":[119,135],"large-scale":[121],"datasets,":[122],"NTU-RGB+D":[123],"Kinetics-Skeleton,":[125],"indicate":[126],"our":[128],"network":[129],"achieves":[130],"accuracy":[131],"improvement":[132],"(about":[133],"1%":[134],"datasets)":[138],"over":[139],"previous":[140],"methods.":[141]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}