{"id":"https://openalex.org/W4402915528","doi":"https://doi.org/10.1109/icip51287.2024.10648200","title":"MVAFormer: RGB-Based Multi-View Spatio-Temporal Action Recognition with Transformer","display_name":"MVAFormer: RGB-Based Multi-View Spatio-Temporal Action Recognition with Transformer","publication_year":2024,"publication_date":"2024-09-27","ids":{"openalex":"https://openalex.org/W4402915528","doi":"https://doi.org/10.1109/icip51287.2024.10648200"},"language":"en","primary_location":{"id":"doi:10.1109/icip51287.2024.10648200","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icip51287.2024.10648200","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Image Processing (ICIP)","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/A5102511251","display_name":"Taiga Yamane","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Taiga Yamane","raw_affiliation_strings":["NTT Corporation,NTT Human Informatics Laboratories,Japan"],"affiliations":[{"raw_affiliation_string":"NTT Corporation,NTT Human Informatics Laboratories,Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063110877","display_name":"Satoshi Suzuki","orcid":"https://orcid.org/0000-0002-1423-3767"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Satoshi Suzuki","raw_affiliation_strings":["NTT Corporation,NTT Human Informatics Laboratories,Japan"],"affiliations":[{"raw_affiliation_string":"NTT Corporation,NTT Human Informatics Laboratories,Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060644399","display_name":"Ryo Masumura","orcid":"https://orcid.org/0000-0002-2415-4149"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryo Masumura","raw_affiliation_strings":["NTT Corporation,NTT Human Informatics Laboratories,Japan"],"affiliations":[{"raw_affiliation_string":"NTT Corporation,NTT Human Informatics Laboratories,Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050560752","display_name":"Shotaro Tora","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shotaro Tora","raw_affiliation_strings":["NTT Corporation,NTT Human Informatics Laboratories,Japan"],"affiliations":[{"raw_affiliation_string":"NTT Corporation,NTT Human Informatics Laboratories,Japan","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5102511251"],"corresponding_institution_ids":["https://openalex.org/I2251713219"],"apc_list":null,"apc_paid":null,"fwci":0.4806,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.64474326,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"332","last_page":"338"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9958000183105469,"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":0.9958000183105469,"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":0.9932000041007996,"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":0.991100013256073,"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":0.7026495933532715},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5876011252403259},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5586672425270081},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.541058361530304},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5351930856704712},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4467678964138031},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.43604129552841187},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1031421422958374},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.07714647054672241},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.07436278462409973}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7026495933532715},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5876011252403259},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5586672425270081},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.541058361530304},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5351930856704712},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4467678964138031},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43604129552841187},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1031421422958374},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.07714647054672241},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.07436278462409973},{"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:10.1109/icip51287.2024.10648200","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icip51287.2024.10648200","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1927052826","https://openalex.org/W2155268664","https://openalex.org/W2618530766","https://openalex.org/W2618799552","https://openalex.org/W2625366777","https://openalex.org/W2894942405","https://openalex.org/W2908510526","https://openalex.org/W2963524571","https://openalex.org/W2964134613","https://openalex.org/W2981851019","https://openalex.org/W2990503944","https://openalex.org/W2991451943","https://openalex.org/W3021385015","https://openalex.org/W3094502228","https://openalex.org/W3096609285","https://openalex.org/W3107147550","https://openalex.org/W3113370935","https://openalex.org/W3166990223","https://openalex.org/W3173407577","https://openalex.org/W3203567247","https://openalex.org/W3203634062","https://openalex.org/W3207615232","https://openalex.org/W4221155627","https://openalex.org/W4221167396","https://openalex.org/W4385245566","https://openalex.org/W6620707391","https://openalex.org/W6739901393","https://openalex.org/W6757817989","https://openalex.org/W6776881853","https://openalex.org/W6784333009","https://openalex.org/W6787254463","https://openalex.org/W6796779252","https://openalex.org/W6802311648","https://openalex.org/W6810265253"],"related_works":["https://openalex.org/W2486460843","https://openalex.org/W2168109476","https://openalex.org/W1968121071","https://openalex.org/W2020254986","https://openalex.org/W2686985752","https://openalex.org/W1992540108","https://openalex.org/W2074225891","https://openalex.org/W2760769977","https://openalex.org/W2004108207","https://openalex.org/W4283332100"],"abstract_inverted_index":{"Multi-view":[0],"action":[1,62,76,84,97],"recognition":[2,77,98],"aims":[3],"to":[4,71,119,162],"recognize":[5],"human":[6],"actions":[7],"using":[8,174],"multiple":[9,34,167],"camera":[10],"views":[11,161,168],"and":[12,159],"deals":[13],"with":[14,126],"occlusion":[15],"caused":[16],"by":[17,32,186],"obstacles":[18],"or":[19],"crowds.":[20],"In":[21,106,117],"this":[22,90,92],"task,":[23],"cooperation":[24,43,113,138],"among":[25,115],"views,":[26,35],"which":[27,81,122,143],"generates":[28],"a":[29,60,95,110,175],"joint":[30],"representation":[31],"combining":[33],"is":[36,85],"vital.":[37],"Previous":[38],"studies":[39],"have":[40],"explored":[41],"promising":[42],"methods":[44,51],"for":[45,100,136,156],"improving":[46],"performance.":[47],"However,":[48],"since":[49],"their":[50],"focus":[52],"only":[53],"on":[54,190],"the":[55,72,101,133,140,145,154,157,164,183,191],"task":[56],"setting":[57],"of":[58,172],"recognizing":[59],"single":[61],"from":[63],"an":[64],"entire":[65],"video,":[66],"they":[67],"are":[68],"not":[69],"applicable":[70],"recently":[73],"popular":[74],"spatio-temporal":[75],"(STAR)":[78],"setting,":[79,103,142],"in":[80,139,149],"each":[82],"person\u2019s":[83],"recognized":[86],"sequentially.":[87],"To":[88],"address":[89],"problem,":[91],"paper":[93],"proposes":[94],"multi-view":[96],"method":[99],"STAR":[102,141],"called":[104],"MVAFormer.":[105],"MVAFormer,":[107],"we":[108,152],"introduce":[109],"novel":[111],"transformer-based":[112],"module":[114,131],"views.":[116],"contrast":[118],"previous":[120],"studies,":[121],"utilize":[123],"embedding":[124],"vectors":[125],"lost":[127],"spatial":[128,146],"information,":[129],"our":[130,150],"utilizes":[132],"feature":[134],"map":[135],"effective":[137],"preserves":[144],"information.":[147],"Furthermore,":[148],"module,":[151],"divide":[153],"self-attention":[155],"same":[158],"different":[160],"model":[163],"relationship":[165],"between":[166],"effectively.":[169],"The":[170],"results":[171],"experiments":[173],"newly":[176],"collected":[177],"dataset":[178],"demonstrate":[179],"that":[180],"MVAFormer":[181],"outperforms":[182],"comparison":[184],"baselines":[185],"approximately":[187],"4.4":[188],"points":[189],"F-measure.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-04-22T08:38:42.863108","created_date":"2025-10-10T00:00:00"}