{"id":"https://openalex.org/W4313050547","doi":"https://doi.org/10.1109/icpr56361.2022.9956108","title":"A Graph Convolutional Network with Early Attention Module for Skeleton-based Action Prediction","display_name":"A Graph Convolutional Network with Early Attention Module for Skeleton-based Action Prediction","publication_year":2022,"publication_date":"2022-08-21","ids":{"openalex":"https://openalex.org/W4313050547","doi":"https://doi.org/10.1109/icpr56361.2022.9956108"},"language":"en","primary_location":{"id":"doi:10.1109/icpr56361.2022.9956108","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956108","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":"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/A5101969200","display_name":"Cuiwei Liu","orcid":"https://orcid.org/0000-0003-4279-4841"},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cuiwei Liu","raw_affiliation_strings":["Shenyang Aerospace University,School of Computer Science,Shenyang,China","School of Computer Science, Shenyang Aerospace University, Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University,School of Computer Science,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]},{"raw_affiliation_string":"School of Computer Science, Shenyang Aerospace University, Shenyang, China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101807932","display_name":"Xiaoxue Zhao","orcid":"https://orcid.org/0000-0002-1358-2228"},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoxue Zhao","raw_affiliation_strings":["Shenyang Aerospace University,School of Computer Science,Shenyang,China","School of Computer Science, Shenyang Aerospace University, Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University,School of Computer Science,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]},{"raw_affiliation_string":"School of Computer Science, Shenyang Aerospace University, Shenyang, China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063569052","display_name":"Zhuo Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuo Yan","raw_affiliation_strings":["Shenyang Aerospace University,School of Artificial Intelligence,Shenyang,China","School of Artificial Intelligence, Shenyang Aerospace University, Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University,School of Artificial Intelligence,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]},{"raw_affiliation_string":"School of Artificial Intelligence, Shenyang Aerospace University, Shenyang, China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051849172","display_name":"Youzhi Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Youzhi Jiang","raw_affiliation_strings":["Shenyang Aerospace University,School of Computer Science,Shenyang,China","School of Computer Science, Shenyang Aerospace University, Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University,School of Computer Science,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]},{"raw_affiliation_string":"School of Computer Science, Shenyang Aerospace University, Shenyang, China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101798674","display_name":"Xiangbin Shi","orcid":"https://orcid.org/0000-0001-7028-3649"},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangbin Shi","raw_affiliation_strings":["Shenyang Aerospace University,School of Computer Science,Shenyang,China","School of Computer Science, Shenyang Aerospace University, Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University,School of Computer Science,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]},{"raw_affiliation_string":"School of Computer Science, Shenyang Aerospace University, Shenyang, China","institution_ids":["https://openalex.org/I125904092"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I125904092"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1266","last_page":"1272"},"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":0.9930999875068665,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9865999817848206,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8705326914787292},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7818837761878967},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6648410558700562},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6096364855766296},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.5961004495620728},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5307326912879944},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.5285170078277588},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5263672471046448},{"id":"https://openalex.org/keywords/attention-network","display_name":"Attention network","score":0.5053218007087708},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.49674302339553833},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4422527849674225},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4002417325973511},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2445705235004425},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1317211389541626},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.06388559937477112}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8705326914787292},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7818837761878967},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6648410558700562},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6096364855766296},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.5961004495620728},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5307326912879944},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.5285170078277588},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5263672471046448},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.5053218007087708},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.49674302339553833},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4422527849674225},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4002417325973511},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2445705235004425},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1317211389541626},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.06388559937477112},{"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},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr56361.2022.9956108","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956108","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":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W66452226","https://openalex.org/W2099634219","https://openalex.org/W2118527252","https://openalex.org/W2147615062","https://openalex.org/W2198577854","https://openalex.org/W2472986212","https://openalex.org/W2551745161","https://openalex.org/W2739251211","https://openalex.org/W2756453812","https://openalex.org/W2788787949","https://openalex.org/W2810685774","https://openalex.org/W2885940446","https://openalex.org/W2900585237","https://openalex.org/W2904794088","https://openalex.org/W2914177721","https://openalex.org/W2945358262","https://openalex.org/W2960406243","https://openalex.org/W2962722475","https://openalex.org/W2964134613","https://openalex.org/W2964840771","https://openalex.org/W2971486392","https://openalex.org/W2989839235","https://openalex.org/W2993402715","https://openalex.org/W2996249958","https://openalex.org/W3006892894","https://openalex.org/W3035050855","https://openalex.org/W3092336341","https://openalex.org/W3167468340","https://openalex.org/W3175259380","https://openalex.org/W3190950662","https://openalex.org/W6753525637","https://openalex.org/W6758943949","https://openalex.org/W6762530125","https://openalex.org/W6765212448","https://openalex.org/W6800195368"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W2004108207","https://openalex.org/W4303411729","https://openalex.org/W2182357018","https://openalex.org/W4211202157","https://openalex.org/W3128220219","https://openalex.org/W3104886537","https://openalex.org/W4287603302","https://openalex.org/W3200983765"],"abstract_inverted_index":{"This":[0],"paper":[1],"addresses":[2],"the":[3,13,17,32,44,54,62,74,96,137,156,167],"problem":[4],"of":[5,47,61,71,102,123,159],"skeleton-based":[6],"action":[7,14,24,34,48,55,64,138,176],"prediction,":[8],"which":[9,119],"aims":[10],"to":[11,31,39,43,128,135,150],"predict":[12],"label":[15],"when":[16],"skeleton":[18],"sequence":[19,79],"is":[20,27,164],"partially":[21],"observed.":[22],"The":[23,50,161],"prediction":[25,56],"task":[26],"more":[28],"challenging":[29],"compared":[30],"after-the-fact":[33],"recognition":[35],"since":[36],"it":[37],"needs":[38],"make":[40],"decisions":[41],"according":[42],"beginning":[45,157],"part":[46],"executions.":[49],"existing":[51],"methods":[52],"improve":[53],"performance":[57,174],"by":[58,94],"taking":[59],"advantage":[60],"global":[63],"knowledge":[65],"in":[66,99],"full":[67,83],"sequences,":[68],"and":[69,80,171],"some":[70],"them":[72],"require":[73],"correspondence":[75],"between":[76],"a":[77,91,110,121],"partial":[78],"its":[81],"associated":[82],"sequence.":[84],"In":[85,133],"this":[86],"paper,":[87],"we":[88,144],"step":[89],"towards":[90],"new":[92],"direction":[93],"exploiting":[95],"discriminative":[97,153],"information":[98],"early":[100,147],"observations":[101,154],"actions":[103],"as":[104,106,140,142],"much":[105],"possible.":[107],"We":[108],"propose":[109],"Graph":[111],"Convolutional":[112],"Network":[113],"with":[114],"Early":[115],"Attention":[116],"Module":[117],"(GCN-EAM),":[118],"employs":[120],"series":[122],"spatial-temporal":[124],"graph":[125],"convolution":[126],"blocks":[127],"extract":[129],"features":[130],"from":[131],"skeletons.":[132],"order":[134],"infer":[136],"category":[139],"fast":[141],"possible,":[143],"introduce":[145],"an":[146],"attention":[148],"module":[149],"adaptively":[151],"emphasize":[152],"at":[155],"stage":[158],"actions.":[160],"proposed":[162],"method":[163],"evaluated":[165],"on":[166],"large-scale":[168],"NTU-RGB+D":[169],"dataset":[170],"achieves":[172],"excellent":[173],"for":[175],"prediction.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}