{"id":"https://openalex.org/W3090167799","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207383","title":"M3LA: A Novel Approach Based on Encoder-Decoder with Attention Framework for Multi-modal Multi-label Learning","display_name":"M3LA: A Novel Approach Based on Encoder-Decoder with Attention Framework for Multi-modal Multi-label Learning","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3090167799","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207383","mag":"3090167799"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207383","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207383","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/A5041878300","display_name":"Yinlong Zhu","orcid":"https://orcid.org/0000-0002-9207-2452"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yinlong Zhu","raw_affiliation_strings":["Computer Science and Technology, Nanjing University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Computer Science and Technology, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100388094","display_name":"Yi Zhang","orcid":"https://orcid.org/0000-0002-0110-0770"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Zhang","raw_affiliation_strings":["Computer Science and Technology, Nanjing University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Computer Science and Technology, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5041878300"],"corresponding_institution_ids":["https://openalex.org/I36399199"],"apc_list":null,"apc_paid":null,"fwci":0.1326,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.54958406,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"32","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9909999966621399,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9887999892234802,"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":0.7821643352508545},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.7183401584625244},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.686075747013092},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5945816040039062},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5698846578598022},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5662668347358704},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5329393148422241},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.5294653177261353},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5182533264160156},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.4995689392089844},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.4971521198749542},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.41557013988494873},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3840813636779785},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32695502042770386},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13189300894737244}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7821643352508545},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.7183401584625244},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.686075747013092},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5945816040039062},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5698846578598022},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5662668347358704},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5329393148422241},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.5294653177261353},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5182533264160156},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.4995689392089844},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.4971521198749542},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.41557013988494873},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3840813636779785},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32695502042770386},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13189300894737244},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9207383","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207383","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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":27,"referenced_works":["https://openalex.org/W1753402186","https://openalex.org/W1777628566","https://openalex.org/W1996326832","https://openalex.org/W1999954155","https://openalex.org/W2029517229","https://openalex.org/W2052684427","https://openalex.org/W2114315281","https://openalex.org/W2120264885","https://openalex.org/W2130942839","https://openalex.org/W2133564696","https://openalex.org/W2147527908","https://openalex.org/W2156935079","https://openalex.org/W2157554677","https://openalex.org/W2584931758","https://openalex.org/W2791383101","https://openalex.org/W2808958151","https://openalex.org/W2885073348","https://openalex.org/W2951527505","https://openalex.org/W2964199361","https://openalex.org/W2964308564","https://openalex.org/W2964900155","https://openalex.org/W2964959430","https://openalex.org/W6677544078","https://openalex.org/W6679434410","https://openalex.org/W6679436768","https://openalex.org/W6682137061","https://openalex.org/W6749167903"],"related_works":["https://openalex.org/W73545470","https://openalex.org/W4224266612","https://openalex.org/W2383394264","https://openalex.org/W4320153225","https://openalex.org/W4293261942","https://openalex.org/W3125968744","https://openalex.org/W203959209","https://openalex.org/W2110287964","https://openalex.org/W2167701463","https://openalex.org/W4307407935"],"abstract_inverted_index":{"With":[0],"the":[1,9,13,44,51,60,68,83,88,97,140,146,152,159,169,180,195,198,201,208,216,219],"exponential":[2],"growth":[3],"of":[4,12,90,134,194,200,218],"digital":[5],"multimedia":[6],"resources,":[7],"in":[8],"real-world,":[10],"most":[11,98,209],"data":[14],"are":[15],"represented":[16],"as":[17,177],"a":[18,33,75,112,164],"multi-modal":[19],"form":[20],"and":[21,48,79,93,156,197,206,230],"usually":[22],"with":[23,123,142,232],"multiple":[24],"semantic":[25],"labels.":[26,54,157],"Nowadays,":[27],"Multi-modal":[28,116],"Multi-label":[29,117],"learning":[30],"has":[31],"become":[32],"very":[34],"hot":[35],"topic.":[36],"However,":[37],"previous":[38],"methods":[39],"either":[40],"have":[41],"not":[42],"considered":[43,59],"relation":[45,153],"between":[46,77,154],"modalities":[47,155],"labels":[49],"or":[50],"correlation":[52,69,76,165,170],"among":[53,70,171],"In":[55],"this":[56],"paper,":[57],"we":[58,110,162,222],"following":[61],"three":[62],"questions:":[63],"(1)":[64],"How":[65],"to":[66,95,167],"model":[67,151],"labels?":[71],"(2)":[72],"Is":[73],"there":[74],"modality":[78],"label?":[80],"(3)":[81],"Whether":[82],"modal":[84,100],"input":[85,101],"order":[86],"affects":[87],"prediction":[89,189,199,211],"individual":[91],"instance,":[92],"how":[94],"find":[96],"appropriate":[99],"sequence":[102],"for":[103,115],"each":[104],"instance?":[105],"To":[106,214],"solve":[107],"above":[108],"problems,":[109],"proposed":[111,220],"novel":[113],"method":[114],"learning(MMML),":[118],"which":[119,173],"based":[120],"on":[121,145,224],"Encoder-Decoder":[122,141],"attention":[124,143],"framwork":[125],"named":[126],"MMML-Attention(M3LA).":[127],"The":[128],"M3LA":[129,149],"takes":[130],"into":[131],"account":[132],"all":[133],"these":[135],"issues.":[136],"Specifically,":[137],"benefit":[138],"from":[139],"structure,":[144],"one":[147],"hand,":[148,161],"can":[150,174],"On":[158],"other":[160],"introduce":[163],"matrix":[166],"learn":[168],"labels,":[172],"be":[175,185],"obtained":[176],"parameter":[178],"through":[179],"training":[181],"process.":[182],"It":[183],"should":[184],"mentioned":[186],"that":[187],"label":[188,202],"occurs":[190],"at":[191],"every":[192],"step":[193],"decoder,":[196],"is":[203,212],"constantly":[204],"corrected":[205],"then":[207],"accurate":[210],"obtained.":[213],"validate":[215],"effectiveness":[217],"method,":[221],"expermiented":[223],"widely":[225],"used":[226],"several":[227],"benchmark":[228],"datasets":[229],"compared":[231],"state-of-art":[233],"approaches.":[234]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}