{"id":"https://openalex.org/W2526740045","doi":"https://doi.org/10.1145/2964284.2986910","title":"Emerging Topics in Learning from Noisy and Missing Data","display_name":"Emerging Topics in Learning from Noisy and Missing Data","publication_year":2016,"publication_date":"2016-09-29","ids":{"openalex":"https://openalex.org/W2526740045","doi":"https://doi.org/10.1145/2964284.2986910","mag":"2526740045"},"language":"en","primary_location":{"id":"doi:10.1145/2964284.2986910","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2964284.2986910","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM international conference on Multimedia","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.research.ed.ac.uk/en/publications/6424e301-4c9c-4060-8676-670b60ae3025","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066621495","display_name":"Xavier Alameda-Pineda","orcid":"https://orcid.org/0000-0002-5354-1084"},"institutions":[{"id":"https://openalex.org/I193223587","display_name":"University of Trento","ror":"https://ror.org/05trd4x28","country_code":"IT","type":"education","lineage":["https://openalex.org/I193223587"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Xavier Alameda-Pineda","raw_affiliation_strings":["University of Trento, Trento, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Trento, Trento, Italy","institution_ids":["https://openalex.org/I193223587"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087823932","display_name":"Timothy M. Hospedales","orcid":"https://orcid.org/0000-0003-4867-7486"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Timothy M. Hospedales","raw_affiliation_strings":["Queen Mary University London, London, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Queen Mary University London, London, United Kingdom","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065059558","display_name":"Elisa Ricci","orcid":"https://orcid.org/0000-0002-0228-1147"},"institutions":[{"id":"https://openalex.org/I2277624104","display_name":"Fondazione Bruno Kessler","ror":"https://ror.org/01j33xk10","country_code":"IT","type":"facility","lineage":["https://openalex.org/I2277624104"]},{"id":"https://openalex.org/I27483092","display_name":"University of Perugia","ror":"https://ror.org/00x27da85","country_code":"IT","type":"education","lineage":["https://openalex.org/I27483092"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Elisa Ricci","raw_affiliation_strings":["Fondazione Bruno Kessler / University of Perugia, Trento / Perugia, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fondazione Bruno Kessler / University of Perugia, Trento / Perugia, Italy","institution_ids":["https://openalex.org/I2277624104","https://openalex.org/I27483092"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027171279","display_name":"Nicu Sebe","orcid":"https://orcid.org/0000-0002-6597-7248"},"institutions":[{"id":"https://openalex.org/I193223587","display_name":"University of Trento","ror":"https://ror.org/05trd4x28","country_code":"IT","type":"education","lineage":["https://openalex.org/I193223587"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Nicu Sebe","raw_affiliation_strings":["University of Trento, Trento, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Trento, Trento, Italy","institution_ids":["https://openalex.org/I193223587"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100444820","display_name":"Xiaogang Wang","orcid":"https://orcid.org/0000-0002-7929-5889"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Xiaogang Wang","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1469","last_page":"1470"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9997000098228455,"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/T10331","display_name":"Video Surveillance and Tracking Methods","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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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":0.8769956231117249},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6452251672744751},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.580497682094574},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5622199177742004},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5525979399681091},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5426831841468811},{"id":"https://openalex.org/keywords/dilemma","display_name":"Dilemma","score":0.5309191346168518},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5296656489372253},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.4904420077800751},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48418524861335754},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.42422887682914734}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8769956231117249},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6452251672744751},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.580497682094574},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5622199177742004},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5525979399681091},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5426831841468811},{"id":"https://openalex.org/C2778496695","wikidata":"https://www.wikidata.org/wiki/Q254128","display_name":"Dilemma","level":2,"score":0.5309191346168518},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5296656489372253},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.4904420077800751},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48418524861335754},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.42422887682914734},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/2964284.2986910","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2964284.2986910","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th ACM international conference on Multimedia","raw_type":"proceedings-article"},{"id":"pmh:oai:iris.unitn.it:11572/195226","is_oa":false,"landing_page_url":"http://hdl.handle.net/11572/195226","pdf_url":null,"source":{"id":"https://openalex.org/S4306401913","display_name":"Institutional Research Information System (Universit\u00e0 degli Studi di Trento)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I193223587","host_organization_name":"University of Trento","host_organization_lineage":["https://openalex.org/I193223587"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:pure.ed.ac.uk:openaire/6424e301-4c9c-4060-8676-670b60ae3025","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/6424e301-4c9c-4060-8676-670b60ae3025","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","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":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Alameda-Pineda, X, Hospedales, T M, Ricci, E, Sebe, N & Wang, X 2016, Emerging Topics in Learning from Noisy and Missing Data. in Proceedings of the 2016 ACM on Multimedia Conference. MM '16, New York, NY, USA, pp. 1469-1470, ACM MULTIMEDIA CONFERENCE 2016, Amsterdam, Netherlands, 15/10/16. https://doi.org/10.1145/2964284.2986910","raw_type":"contributionToPeriodical"},{"id":"pmh:oai:pure.ed.ac.uk:publications/6424e301-4c9c-4060-8676-670b60ae3025","is_oa":false,"landing_page_url":"http://dl.acm.org/citation.cfm?id=2986910","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""}],"best_oa_location":{"id":"pmh:oai:pure.ed.ac.uk:openaire/6424e301-4c9c-4060-8676-670b60ae3025","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/6424e301-4c9c-4060-8676-670b60ae3025","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","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":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Alameda-Pineda, X, Hospedales, T M, Ricci, E, Sebe, N & Wang, X 2016, Emerging Topics in Learning from Noisy and Missing Data. in Proceedings of the 2016 ACM on Multimedia Conference. MM '16, New York, NY, USA, pp. 1469-1470, ACM MULTIMEDIA CONFERENCE 2016, Amsterdam, Netherlands, 15/10/16. https://doi.org/10.1145/2964284.2986910","raw_type":"contributionToPeriodical"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W94414152","https://openalex.org/W1921293667","https://openalex.org/W1973878427","https://openalex.org/W2141350700","https://openalex.org/W2430424407","https://openalex.org/W2472200183","https://openalex.org/W4234552385"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W4312814274","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W2353836703"],"abstract_inverted_index":{"While":[0],"vital":[1],"for":[2,89],"handling":[3],"most":[4],"multimedia":[5],"and":[6,32,86,120],"computer":[7],"vision":[8],"problems,":[9],"collecting":[10],"large":[11,31],"scale":[12],"fully":[13,64],"annotated":[14,65,155],"datasets":[15,27],"is":[16],"a":[17,67,92,109],"resource-consuming,":[18],"often":[19],"unaffordable":[20],"task.":[21],"Indeed,":[22],"on":[23,50],"the":[24,42,51,71,78,114,150],"one":[25],"hand":[26,53],"need":[28],"to":[29,101,112,128],"be":[30,56,63],"variate":[33],"enough":[34,58],"so":[35,59],"that":[36,60],"learning":[37,76],"strategies":[38],"can":[39,62],"successfully":[40],"exploit":[41],"variability":[43,119],"inherently":[44],"present":[45,141],"in":[46,122],"real":[47],"data,":[48],"but":[49],"other":[52],"they":[54,61],"should":[55],"small":[57],"at":[66],"reasonable":[68],"cost.":[69],"With":[70],"overwhelming":[72],"success":[73],"of":[74,81,152],"(deep)":[75],"methods,":[77],"traditional":[79],"problem":[80],"balancing":[82],"between":[83,117],"dataset":[84],"dimensions":[85],"resources":[87],"needed":[88],"annotations":[90],"became":[91],"full-fledged":[93],"dilemma.":[94],"In":[95,136],"this":[96,137],"context,":[97],"methodological":[98],"approaches":[99],"able":[100,127],"deal":[102,129],"with":[103,130],"partially":[104],"described":[105],"data":[106,118,156],"sets":[107],"represent":[108],"one-of-a-kind":[110],"opportunity":[111],"find":[113],"right":[115],"balance":[116],"resource-consumption":[121],"annotation.":[123],"These":[124],"include":[125],"methods":[126],"noisy,":[131,153],"weak":[132],"or":[133],"partial":[134],"annotations.":[135],"tutorial":[138],"we":[139],"will":[140],"several":[142],"recent":[143],"methodologies":[144],"addressing":[145],"different":[146],"visual":[147],"tasks":[148],"under":[149],"assumption":[151],"weakly":[154],"sets.":[157]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
