{"id":"https://openalex.org/W4206618247","doi":"https://doi.org/10.1109/bigdata52589.2021.9671665","title":"Improving Sign Video Modeling Using Graph Neural Network","display_name":"Improving Sign Video Modeling Using Graph Neural Network","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4206618247","doi":"https://doi.org/10.1109/bigdata52589.2021.9671665"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671665","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671665","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"2021 IEEE International Conference on Big Data (Big Data)","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/A5048475146","display_name":"Al Amin Hosain","orcid":null},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Al Amin Hosain","raw_affiliation_strings":["Department of Computer Science, George Mason University, Fairfax, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, George Mason University, Fairfax, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006581225","display_name":"Huzefa Rangwala","orcid":"https://orcid.org/0000-0003-0435-0035"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Huzefa Rangwala","raw_affiliation_strings":["Department of Computer Science, George Mason University, Fairfax, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, George Mason University, Fairfax, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086078885","display_name":"Jana Ko\u0161eck\u00e1","orcid":"https://orcid.org/0000-0003-4619-3277"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jana Kosecka","raw_affiliation_strings":["Department of Computer Science, George Mason University, Fairfax, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, George Mason University, Fairfax, USA","institution_ids":["https://openalex.org/I162714631"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5048475146"],"corresponding_institution_ids":["https://openalex.org/I162714631"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16521739,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"abs 1706 3762","issue":null,"first_page":"4506","last_page":"4513"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9994000196456909,"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":0.9952999949455261,"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":0.8541090488433838},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7315351366996765},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7218598127365112},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5848448276519775},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5654662847518921},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5469751954078674},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4433262348175049},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4206756055355072},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.41387850046157837},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.41017383337020874}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8541090488433838},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7315351366996765},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7218598127365112},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5848448276519775},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5654662847518921},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5469751954078674},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4433262348175049},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4206756055355072},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.41387850046157837},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.41017383337020874},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671665","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671665","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"2021 IEEE International Conference on Big Data (Big Data)","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":39,"referenced_works":["https://openalex.org/W581810680","https://openalex.org/W1522301498","https://openalex.org/W1969724184","https://openalex.org/W1983364832","https://openalex.org/W1984588366","https://openalex.org/W2000761799","https://openalex.org/W2014233635","https://openalex.org/W2194775991","https://openalex.org/W2204609240","https://openalex.org/W2463640844","https://openalex.org/W2883429621","https://openalex.org/W2903314716","https://openalex.org/W2962730651","https://openalex.org/W2963524571","https://openalex.org/W2963708869","https://openalex.org/W2964015378","https://openalex.org/W2964304707","https://openalex.org/W3002681378","https://openalex.org/W3009828227","https://openalex.org/W3046544838","https://openalex.org/W3096803658","https://openalex.org/W3120017044","https://openalex.org/W3128053279","https://openalex.org/W3175333328","https://openalex.org/W3182659197","https://openalex.org/W3184215204","https://openalex.org/W3186585822","https://openalex.org/W3188599010","https://openalex.org/W3197520524","https://openalex.org/W3210279979","https://openalex.org/W4288419263","https://openalex.org/W4385245566","https://openalex.org/W6631190155","https://openalex.org/W6726873649","https://openalex.org/W6739901393","https://openalex.org/W6748370023","https://openalex.org/W6756633688","https://openalex.org/W6760045743","https://openalex.org/W6796527748"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W4246352526","https://openalex.org/W2121910908","https://openalex.org/W915438175","https://openalex.org/W4230315250"],"abstract_inverted_index":{"In":[0],"this":[1],"work,":[2],"we":[3],"present":[4],"an":[5,41,119,164],"ensemble":[6,165,176],"based":[7,102,144],"sign":[8,33,43,92,171,185],"video":[9,23,44,109],"recognition":[10,149],"method.":[11],"Our":[12,124,173],"proposed":[13,174],"method":[14,177],"uses":[15,59],"different":[16],"input":[17,42,58,69,85],"representations":[18],"\u2013":[19,30],"such":[20],"as":[21,138],"RGB":[22],"and":[24,51,71,94,97],"body":[25],"key-points":[26],"or":[27],"pose":[28,81,113,121],"data":[29],"to":[31],"model":[32,134,146],"videos":[34],"in":[35,45,163],"a":[36,66,91,108,139],"multi-modal":[37],"manner.":[38],"We":[39],"represent":[40],"two":[46],"ways:":[47],"the":[48,52,130,142,153,160],"dense":[49,57,131,161],"frame":[50,54,68,80],"sparse":[53,84,145,154],"inputs.":[55],"The":[56,83],"3D":[60,132],"Convolutional":[61,99],"Neural":[62],"Network":[63,77,100],"(CNN)":[64],"on":[65,78,182],"64":[67],"window":[70],"Long":[72],"Short":[73],"Term":[74],"Memory":[75],"(LSTM)":[76],"32":[79],"input.":[82],"picks":[86],"5":[87],"representative":[88,105],"frames":[89,106],"from":[90,118],"video,":[93],"utilizes":[95],"CNN":[96,133],"Graph":[98],"(GCN)":[101],"modeling.":[103],"These":[104],"for":[107],"are":[110,116],"selected":[111],"using":[112],"confidences":[114],"that":[115],"obtained":[117],"off-the-shelf":[120],"estimation":[122],"model.":[123],"experimental":[125],"results":[126],"show":[127],"that,":[128],"while":[129],"achieves":[135],"best":[136],"performance":[137],"single":[140],"classifier,":[141],"GCN":[143],"provides":[147],"extra":[148],"capacity.":[150],"More":[151],"specifically,":[152],"modeling":[155,162],"source,":[156],"when":[157],"added":[158],"with":[159],"manner,":[166],"can":[167],"disambiguate":[168],"similar":[169],"looking":[170],"classes.":[172],"multi-source":[175],"outperforms":[178],"several":[179],"state-of-the-art":[180],"methods":[181],"AUTSL":[183],"Turkish":[184],"language":[186],"benchmark":[187],"dataset.":[188]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}