{"id":"https://openalex.org/W2752434477","doi":"https://doi.org/10.1109/icmew.2017.8026278","title":"Learning robust representations using recurrent neural networks for skeleton based action classification and detection","display_name":"Learning robust representations using recurrent neural networks for skeleton based action classification and detection","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2752434477","doi":"https://doi.org/10.1109/icmew.2017.8026278","mag":"2752434477"},"language":"en","primary_location":{"id":"doi:10.1109/icmew.2017.8026278","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2017.8026278","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multimedia &amp; Expo Workshops (ICMEW)","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/A5014269015","display_name":"Hongsong Wang","orcid":"https://orcid.org/0000-0002-9464-1778"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hongsong Wang","raw_affiliation_strings":["Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR)","University of Chinese Academy of Sciences (UCAS)"],"affiliations":[{"raw_affiliation_string":"Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR)","institution_ids":[]},{"raw_affiliation_string":"University of Chinese Academy of Sciences (UCAS)","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5115602506","display_name":"Liang Wang","orcid":"https://orcid.org/0000-0001-5224-8647"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Wang","raw_affiliation_strings":["Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR)","University of Chinese Academy of Sciences (UCAS)"],"affiliations":[{"raw_affiliation_string":"Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR)","institution_ids":[]},{"raw_affiliation_string":"University of Chinese Academy of Sciences (UCAS)","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5014269015"],"corresponding_institution_ids":["https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":0.7282,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.80674188,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"591","last_page":"596"},"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":0.9965000152587891,"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":0.9959999918937683,"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":0.749639630317688},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7022954225540161},{"id":"https://openalex.org/keywords/skeleton","display_name":"Skeleton (computer programming)","score":0.6729645729064941},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.5629875659942627},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5345166325569153},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5048251748085022},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.5031608939170837},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.49161630868911743},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.468630313873291},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4459100365638733},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.44461774826049805},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.42185115814208984},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.35017549991607666},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33300504088401794},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.30457305908203125},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15476584434509277}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.749639630317688},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7022954225540161},{"id":"https://openalex.org/C18969341","wikidata":"https://www.wikidata.org/wiki/Q1169129","display_name":"Skeleton (computer programming)","level":2,"score":0.6729645729064941},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.5629875659942627},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5345166325569153},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5048251748085022},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.5031608939170837},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.49161630868911743},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.468630313873291},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4459100365638733},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.44461774826049805},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.42185115814208984},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35017549991607666},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33300504088401794},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30457305908203125},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15476584434509277},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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:10.1109/icmew.2017.8026278","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2017.8026278","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multimedia &amp; Expo Workshops (ICMEW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"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":32,"referenced_works":["https://openalex.org/W846669277","https://openalex.org/W1446523894","https://openalex.org/W1904365287","https://openalex.org/W1936750108","https://openalex.org/W1950788856","https://openalex.org/W1968073106","https://openalex.org/W1983592444","https://openalex.org/W2031765333","https://openalex.org/W2056339039","https://openalex.org/W2058256495","https://openalex.org/W2060280062","https://openalex.org/W2064675550","https://openalex.org/W2131774270","https://openalex.org/W2132734311","https://openalex.org/W2158301611","https://openalex.org/W2168570452","https://openalex.org/W2184544926","https://openalex.org/W2313903725","https://openalex.org/W2341313195","https://openalex.org/W2510185399","https://openalex.org/W2556782416","https://openalex.org/W2601271987","https://openalex.org/W2606294640","https://openalex.org/W2950568498","https://openalex.org/W2952587893","https://openalex.org/W2964134613","https://openalex.org/W6628555714","https://openalex.org/W6640754710","https://openalex.org/W6679404632","https://openalex.org/W6685213062","https://openalex.org/W6704032731","https://openalex.org/W6736233412"],"related_works":["https://openalex.org/W3082178636","https://openalex.org/W2782041652","https://openalex.org/W2612657834","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W2392157706","https://openalex.org/W2599192953","https://openalex.org/W2952088488","https://openalex.org/W1521968289","https://openalex.org/W3008584592"],"abstract_inverted_index":{"Recently,":[0],"skeleton":[1,15,45],"based":[2,23,46,78],"action":[3,57,122,143],"recognition":[4],"gains":[5],"more":[6,148],"popularity":[7],"due":[8],"to":[9,81,118],"affordable":[10],"depth":[11],"sensors":[12],"and":[13,32,64,102,124,133],"real-time":[14],"estimation":[16],"algorithms.":[17],"Previous":[18],"Recurrent":[19],"Neural":[20],"Networks":[21],"(RNN)":[22],"approaches":[24],"focus":[25],"on":[26,79,127],"modeling":[27],"spatial":[28,103],"configuration":[29],"of":[30,35,43,138],"skeletons":[31,66],"temporal":[33],"evolution":[34],"body":[36],"joints.":[37],"There":[38],"are":[39,67,147],"certain":[40],"intrinsic":[41],"characteristics":[42],"the":[44,50,65,108,115,136,152],"actions.":[47],"For":[48],"example,":[49],"starting":[51,95],"point":[52,96],"may":[53],"be":[54,59],"varied,":[55],"an":[56],"can":[58],"observed":[60],"at":[61],"arbitrary":[62],"viewpoints":[63],"noisy.":[68],"To":[69],"this":[70],"end,":[71],"we":[72],"present":[73],"a":[74],"novel":[75],"end-to-end":[76],"architecture":[77,89],"RNN":[80],"learn":[82],"robust":[83],"representations":[84],"from":[85],"raw":[86],"skeletons.":[87],"The":[88],"includes":[90],"three":[91,110],"new":[92],"layers,":[93],"i.e.,":[94],"transformation":[97,100],"layer,":[98,105],"viewpoint":[99],"layer":[101],"dropout":[104],"which":[106],"address":[107],"corresponding":[109],"problems,":[111],"respectively.":[112],"We":[113],"apply":[114],"proposed":[116],"method":[117],"two":[119,128],"different":[120],"tasks:":[121],"classification":[123],"detection.":[125],"Experiments":[126],"large-scale":[129],"datasets":[130],"(NTU":[131],"RGB+D":[132],"PKU-MMD)":[134],"show":[135],"superiority":[137],"our":[139,145],"model.":[140],"Specially,":[141],"for":[142],"detection,":[144],"results":[146],"than":[149],"33.4%":[150],"higher":[151],"previous":[153],"results.":[154]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
