{"id":"https://openalex.org/W4403791259","doi":"https://doi.org/10.1145/3664647.3681414","title":"Deep Incomplete Multi-View Network Semi-Supervised Multi-Label Learning with Unbiased Loss","display_name":"Deep Incomplete Multi-View Network Semi-Supervised Multi-Label Learning with Unbiased Loss","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4403791259","doi":"https://doi.org/10.1145/3664647.3681414"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3681414","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3681414","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","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":null,"display_name":"Quanjiang Li","orcid":"https://orcid.org/0009-0004-0507-7602"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Quanjiang Li","raw_affiliation_strings":["National University of Defense Technology, Changsha, China"],"raw_orcid":"https://orcid.org/0009-0004-0507-7602","affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074096377","display_name":"Tingjin Luo","orcid":"https://orcid.org/0000-0002-8171-3971"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingjin Luo","raw_affiliation_strings":["National University of Defense Technology, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-8171-3971","affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107882756","display_name":"M. Jiang","orcid":"https://orcid.org/0009-0004-4767-0383"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingdie Jiang","raw_affiliation_strings":["National University of Defense Technology, Changsha, China"],"raw_orcid":"https://orcid.org/0009-0004-4767-0383","affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078697149","display_name":"Jiahui Liao","orcid":null},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiahui Liao","raw_affiliation_strings":["National University of Defense Technology, Changsha, China"],"raw_orcid":"https://orcid.org/0009-0006-2821-4539","affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075737302","display_name":"Zhangqi Jiang","orcid":"https://orcid.org/0000-0003-2675-249X"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhangqi Jiang","raw_affiliation_strings":["National University of Defense Technology, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0003-2675-249X","affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":2.6872,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.91508314,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"9048","last_page":"9056"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9993000030517578,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9993000030517578,"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/T12016","display_name":"Web Data Mining and Analysis","score":0.9661999940872192,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9616000056266785,"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":0.7512478828430176},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.576488196849823},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4616182744503021},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4240630269050598}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7512478828430176},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.576488196849823},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4616182744503021},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4240630269050598}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3664647.3681414","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3681414","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","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":38,"referenced_works":["https://openalex.org/W1666447063","https://openalex.org/W1981613567","https://openalex.org/W2019288156","https://openalex.org/W2031489346","https://openalex.org/W2108013467","https://openalex.org/W2141282920","https://openalex.org/W2155803963","https://openalex.org/W2199534117","https://openalex.org/W2512563520","https://openalex.org/W2787932447","https://openalex.org/W2793189157","https://openalex.org/W2794084401","https://openalex.org/W2808239701","https://openalex.org/W2881233020","https://openalex.org/W2906529026","https://openalex.org/W2954996726","https://openalex.org/W2982112268","https://openalex.org/W2984353870","https://openalex.org/W2990500698","https://openalex.org/W3006634967","https://openalex.org/W3037422790","https://openalex.org/W3122012821","https://openalex.org/W3157662314","https://openalex.org/W3169047433","https://openalex.org/W3201879291","https://openalex.org/W3215900581","https://openalex.org/W4210622929","https://openalex.org/W4226135409","https://openalex.org/W4281691770","https://openalex.org/W4285492263","https://openalex.org/W4290713716","https://openalex.org/W4292387169","https://openalex.org/W4304091711","https://openalex.org/W4361801401","https://openalex.org/W4382467872","https://openalex.org/W4382468418","https://openalex.org/W4388752051","https://openalex.org/W4393160165"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W4321369474","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3046775127","https://openalex.org/W3082895349"],"abstract_inverted_index":{"Due":[0],"to":[1,34,88,101,118,136],"the":[2,35,63,103,113,124,128,138,141,144,147,177,185,200],"explosive":[3],"growth":[4],"in":[5,44],"data":[6,21,130],"sources":[7],"and":[8,26,38,54,122,162,184],"label":[9],"categories,":[10],"multi-view":[11,19],"multi-label":[12,20,149],"learning":[13],"has":[14],"garnered":[15],"widespread":[16],"attention.":[17],"However,":[18],"often":[22],"exhibits":[23],"incomplete":[24],"features":[25,53],"a":[27,77,154],"huge":[28],"number":[29],"of":[30,41,51,92,140,143,170,180,188,202],"unlabeled":[31,171],"instances,":[32],"due":[33],"technical":[36],"limitations":[37],"high":[39],"cost":[40],"manual":[42],"labeling":[43],"practice.":[45],"Learning":[46,84],"for":[47,127],"such":[48],"simultaneous":[49],"missing":[50,93],"view":[52],"labels":[55],"is":[56,98],"crucial":[57],"but":[58],"rarely":[59],"studied,":[60],"particularly":[61],"when":[62],"labeled":[64],"samples":[65],"are":[66],"limited.":[67],"In":[68],"this":[69,73],"paper,":[70],"we":[71,111,152,174],"tackle":[72],"problem":[74],"by":[75,167],"proposing":[76],"novel":[78],"Deep":[79],"Incomplete":[80],"Multi-View":[81],"Semi-Supervised":[82],"Multi-Label":[83],"method":[85],"(DIMvSML).":[86],"Specifically,":[87],"improve":[89,163],"high-level":[90],"representations":[91],"features,":[94],"deep":[95,115],"graph":[96],"network":[97],"firstly":[99],"employed":[100],"recover":[102],"feature":[104,116],"information":[105,121],"with":[106,131,158,205],"structural":[107],"similarity":[108],"relations.":[109],"Meanwhile,":[110],"design":[112,153],"structure-specific":[114],"extractors":[117],"obtain":[119],"discriminative":[120],"preserve":[123],"cross-view":[125],"consistency":[126],"recovered":[129],"instance-level":[132],"contrastive":[133],"loss.":[134],"Furthermore,":[135],"eliminate":[137],"bias":[139],"estimate":[142,156,182],"risk":[145],"that":[146],"semi-supervised":[148],"methods":[150],"minimise,":[151],"safe":[155],"framework":[157],"an":[159],"unbiased":[160],"loss":[161],"its":[164],"empirical":[165],"performance":[166],"using":[168],"pseudo-labels":[169],"data.":[172],"Besides,":[173],"provide":[175],"both":[176],"theoretical":[178],"proof":[179],"better":[181],"variance":[183],"intuitive":[186],"explanation":[187],"our":[189],"debiased":[190],"framework.":[191],"Finally,":[192],"extensive":[193],"experimental":[194],"results":[195],"on":[196],"public":[197],"datasets":[198],"validate":[199],"superiority":[201],"DIMvSML":[203],"compared":[204],"state-of-the-art":[206],"methods.":[207]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
