{"id":"https://openalex.org/W3008538079","doi":"https://doi.org/10.1145/3376067.3376076","title":"Attention-Based Generative Graph Convolutional Network for Skeleton-Based Human Action Recognition","display_name":"Attention-Based Generative Graph Convolutional Network for Skeleton-Based Human Action Recognition","publication_year":2019,"publication_date":"2019-12-20","ids":{"openalex":"https://openalex.org/W3008538079","doi":"https://doi.org/10.1145/3376067.3376076","mag":"3008538079"},"language":"en","primary_location":{"id":"doi:10.1145/3376067.3376076","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3376067.3376076","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd International Conference on Video and Image Processing","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/A5054170567","display_name":"Kai Yang","orcid":"https://orcid.org/0000-0001-6337-1616"},"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":true,"raw_author_name":"Kai Yang","raw_affiliation_strings":["China Mobile Research Institute, Beijing, China"],"affiliations":[{"raw_affiliation_string":"China Mobile Research Institute, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","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"],"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"],"affiliations":[{"raw_affiliation_string":"China Mobile Research Institute, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5054170567"],"corresponding_institution_ids":["https://openalex.org/I180662265"],"apc_list":null,"apc_paid":null,"fwci":0.3037,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.62617405,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9962000250816345,"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":0.9954000115394592,"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":0.7383528351783752},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.6719201803207397},{"id":"https://openalex.org/keywords/skeleton","display_name":"Skeleton (computer programming)","score":0.6028013825416565},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5784516334533691},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5376436114311218},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5365699529647827},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47170186042785645},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.42349737882614136},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.17227599024772644}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7383528351783752},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.6719201803207397},{"id":"https://openalex.org/C18969341","wikidata":"https://www.wikidata.org/wiki/Q1169129","display_name":"Skeleton (computer programming)","level":2,"score":0.6028013825416565},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5784516334533691},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5376436114311218},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5365699529647827},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47170186042785645},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.42349737882614136},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.17227599024772644},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3376067.3376076","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3376067.3376076","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd International Conference on Video and Image Processing","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":22,"referenced_works":["https://openalex.org/W1926645898","https://openalex.org/W2048821851","https://openalex.org/W2468907370","https://openalex.org/W2510185399","https://openalex.org/W2511929793","https://openalex.org/W2519758495","https://openalex.org/W2559085405","https://openalex.org/W2593146028","https://openalex.org/W2603861860","https://openalex.org/W2604321021","https://openalex.org/W2613570903","https://openalex.org/W2778523960","https://openalex.org/W2798644314","https://openalex.org/W2802979841","https://openalex.org/W2940457086","https://openalex.org/W2948058585","https://openalex.org/W2963076818","https://openalex.org/W2964097678","https://openalex.org/W2964134613","https://openalex.org/W2964145825","https://openalex.org/W3098538019","https://openalex.org/W4234552385"],"related_works":["https://openalex.org/W2380075625","https://openalex.org/W2953562271","https://openalex.org/W4293226380","https://openalex.org/W2334655667","https://openalex.org/W4388657984","https://openalex.org/W2999049608","https://openalex.org/W3010741941","https://openalex.org/W3170431411","https://openalex.org/W3101070354","https://openalex.org/W4287181611"],"abstract_inverted_index":{"Skeleton-based":[0],"action":[1,123],"recognition":[2,124],"is":[3,77,100,111],"a":[4],"challenging":[5],"field":[6],"in":[7],"computer":[8],"vision.":[9],"Graph":[10,76],"representations":[11],"of":[12,21,29,72,132],"skeleton":[13,31,74,88],"are":[14,126],"used":[15,102],"to":[16,52,66,92,103,113,128],"learn":[17,53],"the":[18,25,38,54,68,82,115,130,147],"connection":[19,39,57],"patterns":[20,58],"human":[22,30],"joints.":[23],"However,":[24],"fixed":[26],"handcraft":[27],"graph":[28,49,56,108],"topology":[32],"can":[33],"not":[34],"well":[35],"represent":[36],"all":[37],"patterns.":[40],"In":[41,90],"this":[42],"work,":[43],"we":[44],"propose":[45],"an":[46],"end-to-end":[47],"generative":[48,109],"convolution":[50],"network":[51],"joints":[55],"directly":[59],"from":[60,87],"data.":[61],"We":[62],"use":[63],"self":[64],"attention":[65],"construct":[67],"spatial":[69],"adjacency":[70,84],"matrix":[71,85],"each":[73],"frame.":[75],"generated":[78],"progressively":[79],"by":[80],"learning":[81],"weighted":[83],"sequence":[86],"frames.":[89],"order":[91],"extract":[93],"temporal":[94],"dynamics":[95],"effectively,":[96],"velocity":[97],"semantic":[98],"information":[99],"also":[101],"generate":[104],"graphs.":[105],"An":[106],"online":[107],"mechanism":[110],"proposed":[112],"enhance":[114],"model":[116],"adaption.":[117],"Extensive":[118],"experiments":[119],"on":[120],"two":[121],"large-scale":[122],"datasets":[125],"preformed":[127],"verify":[129],"effectiveness":[131],"our":[133,140],"approach.":[134],"The":[135],"comparison":[136],"results":[137],"show":[138],"that":[139],"method":[141],"achieves":[142],"remarkable":[143],"improvement":[144],"compared":[145],"with":[146],"state-of-the-art":[148],"approaches.":[149]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}