{"id":"https://openalex.org/W3207264592","doi":"https://doi.org/10.1145/3469877.3490580","title":"NoisyActions2M: A Multimedia Dataset for Video Understanding from Noisy Labels","display_name":"NoisyActions2M: A Multimedia Dataset for Video Understanding from Noisy Labels","publication_year":2021,"publication_date":"2021-12-01","ids":{"openalex":"https://openalex.org/W3207264592","doi":"https://doi.org/10.1145/3469877.3490580","mag":"3207264592"},"language":"en","primary_location":{"id":"doi:10.1145/3469877.3490580","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3469877.3490580","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Multimedia Asia","raw_type":"proceedings-article"},"type":"preprint","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/A5102792137","display_name":"Mohit Sharma","orcid":"https://orcid.org/0000-0002-5680-9111"},"institutions":[{"id":"https://openalex.org/I4210138565","display_name":"International Institute of Islamic Thought","ror":"https://ror.org/03n60vp23","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210138565"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mohit Sharma","raw_affiliation_strings":["IIIT Delhi, IN"],"affiliations":[{"raw_affiliation_string":"IIIT Delhi, IN","institution_ids":["https://openalex.org/I4210138565"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110809824","display_name":"Raj Aaryaman Patra","orcid":null},"institutions":[{"id":"https://openalex.org/I16292982","display_name":"National Institute of Technology Rourkela","ror":"https://ror.org/011gmn932","country_code":"IN","type":"education","lineage":["https://openalex.org/I16292982"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Raj Aaryaman Patra","raw_affiliation_strings":["National Institute Of Technology,Rourkela, IN"],"affiliations":[{"raw_affiliation_string":"National Institute Of Technology,Rourkela, IN","institution_ids":["https://openalex.org/I16292982"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110332073","display_name":"Harshal Desai","orcid":null},"institutions":[{"id":"https://openalex.org/I187761245","display_name":"National Institute of Technology Jamshedpur","ror":"https://ror.org/01sebzx27","country_code":"IN","type":"education","lineage":["https://openalex.org/I187761245"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Harshal Desai","raw_affiliation_strings":["National Institute of Technology Jamshedpur, IN"],"affiliations":[{"raw_affiliation_string":"National Institute of Technology Jamshedpur, IN","institution_ids":["https://openalex.org/I187761245"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072711888","display_name":"Shruti Vyas","orcid":"https://orcid.org/0000-0002-5591-5086"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shruti Vyas","raw_affiliation_strings":["University of Central Florida, US"],"affiliations":[{"raw_affiliation_string":"University of Central Florida, US","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002721667","display_name":"Yogesh Singh Rawat","orcid":"https://orcid.org/0000-0003-4052-6798"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yogesh Rawat","raw_affiliation_strings":["University of Central Florida, US"],"affiliations":[{"raw_affiliation_string":"University of Central Florida, US","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079357056","display_name":"Rajiv Ratn Shah","orcid":"https://orcid.org/0000-0003-1028-9373"},"institutions":[{"id":"https://openalex.org/I4210138565","display_name":"International Institute of Islamic Thought","ror":"https://ror.org/03n60vp23","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210138565"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rajiv Ratn Shah","raw_affiliation_strings":["IIIT Delhi, IN"],"affiliations":[{"raw_affiliation_string":"IIIT Delhi, IN","institution_ids":["https://openalex.org/I4210138565"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5102792137"],"corresponding_institution_ids":["https://openalex.org/I4210138565"],"apc_list":null,"apc_paid":null,"fwci":0.10221948,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.40734884,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9994999766349792,"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":0.9994999766349792,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.998199999332428,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9977999925613403,"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":0.885643482208252},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7612974643707275},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5933414697647095},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5635726451873779},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.49088817834854126},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4596099853515625},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4595031440258026},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.458830326795578},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.15233197808265686}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.885643482208252},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7612974643707275},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5933414697647095},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5635726451873779},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.49088817834854126},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4596099853515625},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4595031440258026},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.458830326795578},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.15233197808265686},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3469877.3490580","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3469877.3490580","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Multimedia Asia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8199999928474426,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W1861492603","https://openalex.org/W1905722737","https://openalex.org/W1921293667","https://openalex.org/W1927052826","https://openalex.org/W2016053056","https://openalex.org/W2081580037","https://openalex.org/W2081613070","https://openalex.org/W2108598243","https://openalex.org/W2124219775","https://openalex.org/W2126579184","https://openalex.org/W2167460663","https://openalex.org/W2185175083","https://openalex.org/W2250384498","https://openalex.org/W2277195237","https://openalex.org/W2425121537","https://openalex.org/W2524365899","https://openalex.org/W2618574054","https://openalex.org/W2618799552","https://openalex.org/W2619947201","https://openalex.org/W2625366777","https://openalex.org/W2743200750","https://openalex.org/W2763421725","https://openalex.org/W2798963049","https://openalex.org/W2886641317","https://openalex.org/W2962711930","https://openalex.org/W2962762068","https://openalex.org/W2962934715","https://openalex.org/W2963155035","https://openalex.org/W2963293463","https://openalex.org/W2963351448","https://openalex.org/W2963703197","https://openalex.org/W2963916161","https://openalex.org/W2964096266","https://openalex.org/W2964292098","https://openalex.org/W2968101724","https://openalex.org/W2971414575","https://openalex.org/W2971874326","https://openalex.org/W2980037812","https://openalex.org/W2982399380","https://openalex.org/W2994508843","https://openalex.org/W3094717204","https://openalex.org/W3095707208","https://openalex.org/W3095827467","https://openalex.org/W3151336044","https://openalex.org/W3183932535","https://openalex.org/W4297928600"],"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":{"Deep":[0],"learning":[1,130],"has":[2],"shown":[3],"remarkable":[4],"progress":[5],"in":[6,118,128],"a":[7,49,103,125],"wide":[8],"range":[9],"of":[10,15,53,150],"problems.":[11],"However,":[12],"efficient":[13],"training":[14],"such":[16,25],"models":[17,139],"requires":[18],"large-scale":[19],"datasets,":[20,82],"and":[21,30,62,74,84,91,96,115,137],"getting":[22],"annotations":[23,61],"for":[24,44,71,131,144],"datasets":[26],"can":[27,110],"be":[28],"challenging":[29],"costly.":[31],"In":[32],"this":[33,123],"work,":[34],"we":[35],"explore":[36],"user-generated":[37,60],"freely":[38],"available":[39,142,155],"labels":[40],"from":[41],"web":[42],"videos":[43,57],"video":[45,113,132],"understanding.":[46,133],"We":[47,66,86,99,121],"create":[48],"benchmark":[50,126],"dataset":[51,70,109,127],"consisting":[52],"around":[54],"2":[55],"million":[56],"with":[58,78],"associated":[59],"other":[63],"meta":[64],"information.":[65],"utilize":[67],"the":[68,107],"collected":[69],"action":[72],"classification":[73],"demonstrate":[75],"its":[76],"usefulness":[77],"existing":[79],"small-scale":[80],"annotated":[81],"UCF101":[83],"HMDB51.":[85],"study":[87],"different":[88],"loss":[89],"functions":[90],"two":[92],"pretraining":[93],"strategies,":[94],"simple":[95],"self-supervised":[97],"learning.":[98],"also":[100,154],"show":[101],"how":[102],"network":[104],"pretrained":[105],"on":[106],"proposed":[108],"help":[111],"against":[112],"corruption":[114],"label":[116],"noise":[117],"downstream":[119],"datasets.":[120],"present":[122],"as":[124],"noisy":[129],"The":[134],"dataset,":[135],"code,":[136],"trained":[138],"are":[140],"publicly":[141],"here":[143],"future":[145],"research.":[146],"A":[147],"longer":[148],"version":[149],"our":[151],"paper":[152],"is":[153],"here.":[156]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
