{"id":"https://openalex.org/W4406458283","doi":"https://doi.org/10.1109/bigdata62323.2024.10825506","title":"EfficientViT for Video Action Recognition","display_name":"EfficientViT for Video Action Recognition","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406458283","doi":"https://doi.org/10.1109/bigdata62323.2024.10825506"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825506","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825506","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 Big Data (BigData)","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/A5004874255","display_name":"Dingdong Jin","orcid":"https://orcid.org/0000-0002-7773-8965"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Derek Jin","raw_affiliation_strings":["Noble and Greenough School,Dedham,MA,USA,02026"],"affiliations":[{"raw_affiliation_string":"Noble and Greenough School,Dedham,MA,USA,02026","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085737253","display_name":"S. H. Zeng","orcid":"https://orcid.org/0000-0001-6106-7741"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shengyou Zeng","raw_affiliation_strings":["Tradelegs, Inc.,Boston,MA,USA,02135"],"affiliations":[{"raw_affiliation_string":"Tradelegs, Inc.,Boston,MA,USA,02135","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5004874255"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2624,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.58738402,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"7323","last_page":"7326"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9980999827384949,"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":0.9976999759674072,"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":0.7166463732719421},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.62616366147995},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.5462561845779419},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4106951355934143},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3736410140991211}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7166463732719421},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.62616366147995},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.5462561845779419},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4106951355934143},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3736410140991211},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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:10.1109/bigdata62323.2024.10825506","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825506","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 Big Data (BigData)","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":20,"referenced_works":["https://openalex.org/W1522734439","https://openalex.org/W2156303437","https://openalex.org/W2507009361","https://openalex.org/W2963524571","https://openalex.org/W2999905431","https://openalex.org/W3113370935","https://openalex.org/W3175528717","https://openalex.org/W4214612132","https://openalex.org/W4281668357","https://openalex.org/W4285819090","https://openalex.org/W4297821406","https://openalex.org/W4387226763","https://openalex.org/W4387327742","https://openalex.org/W6682864246","https://openalex.org/W6779709467","https://openalex.org/W6787254463","https://openalex.org/W6838669823","https://openalex.org/W6840041335","https://openalex.org/W6843154862","https://openalex.org/W6857703410"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Video":[0],"action":[1],"recognition":[2,18],"is":[3,27],"a":[4,8,28,109],"critical":[5],"challenge":[6],"in":[7],"wide":[9],"range":[10],"of":[11,15,31,41,107],"practical":[12],"applications.":[13],"Most":[14],"the":[16,42,63,98,113],"video":[17,80,100],"models":[19],"are":[20],"computationally":[21],"expensive":[22],"and":[23,52,59],"energy":[24],"intensive.":[25],"EfficientViT":[26,78,91],"new":[29],"family":[30],"vision":[32,46],"transformers":[33],"that":[34],"offer":[35],"high":[36],"efficiency":[37],"while":[38],"maintaining":[39],"state":[40],"art":[43],"accuracy":[44,106],"on":[45,56,69],"tasks":[47],"such":[48],"as":[49],"image":[50],"classification":[51],"semantic":[53],"segmentation":[54],"including":[55],"daily":[57],"devices":[58],"cloud":[60],"computing.":[61],"However,":[62],"effectiveness":[64],"has":[65],"only":[66],"been":[67],"validated":[68],"images":[70],"so":[71],"far.":[72],"In":[73],"this":[74],"project,":[75],"we":[76],"extend":[77],"to":[79],"processing":[81],"by":[82],"replacing":[83],"2D":[84],"convolution":[85],"with":[86,97],"3D":[87],"convolution.":[88],"The":[89],"updated":[90],"model":[92],"B1-r288":[93],"was":[94],"successfully":[95],"trained":[96],"Epic-Kitchens-100":[99],"dataset,":[101],"achieving":[102],"top":[103],"1%":[104],"verb":[105],"27.7%,":[108],"5.4%":[110],"improvement":[111],"than":[112],"original":[114],"model.":[115]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}