{"id":"https://openalex.org/W4377236381","doi":"https://doi.org/10.1145/3581807.3581840","title":"Human Action Recognition Based on Vision Transformer and L2 Regularization","display_name":"Human Action Recognition Based on Vision Transformer and L2 Regularization","publication_year":2022,"publication_date":"2022-11-17","ids":{"openalex":"https://openalex.org/W4377236381","doi":"https://doi.org/10.1145/3581807.3581840"},"language":"en","primary_location":{"id":"doi:10.1145/3581807.3581840","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581807.3581840","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition","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/A5101528623","display_name":"Qiliang Chen","orcid":"https://orcid.org/0000-0002-3896-9365"},"institutions":[{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qiliang Chen","raw_affiliation_strings":["School of Computer and Information Engineering, Xiamen University of Technology, China"],"raw_orcid":"https://orcid.org/0000-0002-3896-9365","affiliations":[{"raw_affiliation_string":"School of Computer and Information Engineering, Xiamen University of Technology, China","institution_ids":["https://openalex.org/I75867142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045464309","display_name":"Hasiqidalatu Tang","orcid":"https://orcid.org/0000-0002-3359-0946"},"institutions":[{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hasiqidalatu Tang","raw_affiliation_strings":["School of Mathematics and Statistics, Xiamen University of Technology, China"],"raw_orcid":"https://orcid.org/0000-0002-3359-0946","affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Xiamen University of Technology, China","institution_ids":["https://openalex.org/I75867142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021487267","display_name":"Jiaxin Cai","orcid":"https://orcid.org/0000-0002-8989-085X"},"institutions":[{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaxin Cai","raw_affiliation_strings":["School of Mathematics and Statistics, Xiamen University of Technology, China"],"raw_orcid":"https://orcid.org/0000-0002-8989-085X","affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Xiamen University of Technology, China","institution_ids":["https://openalex.org/I75867142"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101528623"],"corresponding_institution_ids":["https://openalex.org/I75867142"],"apc_list":null,"apc_paid":null,"fwci":0.4082,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.61706675,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"224","last_page":"228"},"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":0.9976000189781189,"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":0.9921000003814697,"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/overfitting","display_name":"Overfitting","score":0.7785158157348633},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7276436686515808},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.67872154712677},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6709174513816833},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.6459094285964966},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6174688935279846},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5248596668243408},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5156082510948181},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4695131480693817},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4144176244735718},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3226320743560791},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.20676544308662415},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1001926064491272},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.08233824372291565}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7785158157348633},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7276436686515808},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.67872154712677},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6709174513816833},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.6459094285964966},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6174688935279846},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5248596668243408},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5156082510948181},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4695131480693817},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4144176244735718},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3226320743560791},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.20676544308662415},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1001926064491272},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.08233824372291565},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"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:10.1145/3581807.3581840","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581807.3581840","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition","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":9,"referenced_works":["https://openalex.org/W2034443282","https://openalex.org/W2909888383","https://openalex.org/W2963901365","https://openalex.org/W2990714382","https://openalex.org/W3034999503","https://openalex.org/W3040842087","https://openalex.org/W3131297908","https://openalex.org/W4236060337","https://openalex.org/W6833121108"],"related_works":["https://openalex.org/W2365346975","https://openalex.org/W2146076056","https://openalex.org/W2772780115","https://openalex.org/W2385132419","https://openalex.org/W2384093694","https://openalex.org/W1977222486","https://openalex.org/W2187500075","https://openalex.org/W2311479510","https://openalex.org/W2739275700","https://openalex.org/W3106494386"],"abstract_inverted_index":{"In":[0,40,99],"recent":[1],"years,":[2],"the":[3,11,58,68,84,92,112,116,120,123,133,139],"field":[4],"of":[5,13,47,70,78,91],"human":[6,17,48,124],"action":[7,18,49,125],"recognition":[8,19,50,140],"has":[9,20],"been":[10],"focus":[12],"computer":[14],"vision,":[15],"and":[16,35,61,80,86],"a":[21],"good":[22],"prospect":[23],"in":[24,54,63,107],"many":[25],"fields,":[26],"such":[27],"as":[28,67],"security":[29],"state":[30],"monitoring,":[31],"behavior":[32],"characteristics":[33,77],"analysis":[34],"network":[36,65,95],"video":[37,74],"image":[38],"restoration.":[39],"this":[41],"paper,":[42],"based":[43],"on":[44,122],"attention":[45,88],"mechanism":[46,89],"method":[51],"is":[52,105,130],"studied,":[53],"order":[55],"to":[56,110],"improve":[57,138],"model":[59,108,113,135],"accuracy":[60,141],"efficiency":[62],"VIT":[64],"structure":[66],"framework":[69],"feature":[71,97],"extraction,":[72,98],"because":[73],"data":[75],"includes":[76],"time":[79,87],"space,":[81],"so":[82],"choose":[83],"space":[85],"instead":[90],"traditional":[93],"convolution":[94],"for":[96],"addition,":[100],"L2":[101],"weight":[102],"attenuation":[103],"regularization":[104],"introduced":[106],"training":[109,117],"prevent":[111],"from":[114],"overfitting":[115],"data.":[118],"Through":[119],"test":[121],"related":[126],"dataset":[127],"UCF101,":[128],"it":[129],"found":[131],"that":[132],"proposed":[134],"can":[136],"effectively":[137],"compared":[142],"with":[143],"other":[144],"models.":[145]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}