{"id":"https://openalex.org/W3033462708","doi":"https://doi.org/10.1145/3390557.3394129","title":"A Semantics-Guided Graph Convolutional Network for Skeleton-Based Action Recognition","display_name":"A Semantics-Guided Graph Convolutional Network for Skeleton-Based Action Recognition","publication_year":2020,"publication_date":"2020-05-08","ids":{"openalex":"https://openalex.org/W3033462708","doi":"https://doi.org/10.1145/3390557.3394129","mag":"3033462708"},"language":"en","primary_location":{"id":"doi:10.1145/3390557.3394129","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3390557.3394129","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 the 4th International Conference on Innovation in Artificial Intelligence","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/A5085416876","display_name":"Xiaolu Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolu Ding","raw_affiliation_strings":["China Mobile Research Institute, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Mobile Research Institute, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103047029","display_name":"Yang Kai","orcid":"https://orcid.org/0000-0001-8805-8650"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Yang","raw_affiliation_strings":["China Mobile Research Institute, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Mobile Research Institute, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102986531","display_name":"Wai Chen","orcid":"https://orcid.org/0000-0002-1663-2729"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wai Chen","raw_affiliation_strings":["China Mobile Research Institute, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Mobile Research Institute, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I180662265"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"130","last_page":"136"},"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":0.9868999719619751,"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":0.9846000075340271,"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/computer-science","display_name":"Computer science","score":0.7680140733718872},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5451594591140747},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.5142571330070496},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5074569582939148},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49904465675354004},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4758336544036865}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7680140733718872},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5451594591140747},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.5142571330070496},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5074569582939148},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49904465675354004},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4758336544036865},{"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:10.1145/3390557.3394129","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3390557.3394129","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 the 4th International Conference on Innovation in Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6600000262260437,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1926645898","https://openalex.org/W2048821851","https://openalex.org/W2081773958","https://openalex.org/W2184544926","https://openalex.org/W2224196924","https://openalex.org/W2244807774","https://openalex.org/W2510185399","https://openalex.org/W2511929793","https://openalex.org/W2559085405","https://openalex.org/W2593146028","https://openalex.org/W2604321021","https://openalex.org/W2606294640","https://openalex.org/W2613570903","https://openalex.org/W2716916105","https://openalex.org/W2736995747","https://openalex.org/W2776217220","https://openalex.org/W2778523960","https://openalex.org/W2793547936","https://openalex.org/W2798644314","https://openalex.org/W2799062425","https://openalex.org/W2802979841","https://openalex.org/W2963076818","https://openalex.org/W2963346996","https://openalex.org/W2963984147","https://openalex.org/W2964097678","https://openalex.org/W2964113829","https://openalex.org/W2964134613","https://openalex.org/W3098538019","https://openalex.org/W4236965008"],"related_works":["https://openalex.org/W2033914206","https://openalex.org/W2146076056","https://openalex.org/W2163831990","https://openalex.org/W3003836766","https://openalex.org/W2046077695","https://openalex.org/W2378160586","https://openalex.org/W2042327336","https://openalex.org/W2996038082","https://openalex.org/W3047965787","https://openalex.org/W2941155331"],"abstract_inverted_index":{"Action":[0],"recognition":[1],"with":[2],"skeleton":[3,56,72,133],"data":[4,57],"is":[5],"a":[6,66,76,131],"challenging":[7],"task":[8],"in":[9,108],"computer":[10],"vision.":[11],"Graph":[12],"convolutional":[13,80],"networks":[14],"(GCNs),":[15],"which":[16,143],"directly":[17],"model":[18,169],"the":[19,24,39,71,138,164,171],"human":[20],"body":[21],"skeletons":[22],"as":[23],"graph":[25,68,79,91,94,98,103,140,146],"structure,":[26],"have":[27],"achieved":[28],"remarkable":[29],"performance.":[30],"However,":[31],"current":[32],"architectures":[33],"of":[34,43,70,89,166],"GCNs":[35],"are":[36,106],"limited":[37],"to":[38,64,110,116,122,151],"small":[40],"receptive":[41],"field":[42],"convolution":[44,141,147],"filters,":[45],"only":[46],"capturing":[47],"local":[48],"physical":[49],"dependencies":[50,120],"among":[51],"joints":[52],"and":[53,63,101,121,148,155,175],"using":[54],"all":[55],"indiscriminately.":[58],"To":[59],"address":[60],"these":[61,127],"limitations":[62],"achieve":[65],"flexible":[67],"representation":[69],"features,":[73],"we":[74,135],"propose":[75,137],"novel":[77],"semantics-guided":[78,139],"network":[81],"(Sem-GCN)":[82],"for":[83,158],"skeleton-based":[84],"action":[85,159],"recognition.":[86,160],"Three":[87],"types":[88],"semantic":[90,128,145,154],"modules":[92],"(structural":[93],"extraction":[95],"module,":[96],"actional":[97],"inference":[99],"module":[100],"attention":[102],"iteration":[104],"module)":[105],"employed":[107],"Sem-GCN":[109],"aggregate":[111],"L-hop":[112],"joint":[113],"neighbors'":[114],"information,":[115],"capture":[117],"action-specific":[118],"latent":[119],"distribute":[123],"importance":[124],"level.":[125],"Combing":[126],"graphs":[129],"into":[130],"generalized":[132],"graph,":[134],"further":[136],"block,":[142],"stacks":[144],"temporal":[149,156],"convolution,":[150],"learn":[152],"both":[153],"features":[157],"Experimental":[161],"results":[162],"demonstrate":[163],"effectiveness":[165],"our":[167],"proposed":[168],"on":[170],"widely":[172],"used":[173],"NTU":[174],"Kinetics":[176],"datasets.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}