{"id":"https://openalex.org/W4403790976","doi":"https://doi.org/10.1145/3664647.3680793","title":"Multi-Label Learning with Block Diagonal Labels","display_name":"Multi-Label Learning with Block Diagonal Labels","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4403790976","doi":"https://doi.org/10.1145/3664647.3680793"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3680793","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3680793","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":"conference-paper","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/A5002748588","display_name":"Leqi Shen","orcid":"https://orcid.org/0000-0002-7742-9142"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Leqi Shen","raw_affiliation_strings":["School of Software, BNRist, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7742-9142","affiliations":[{"raw_affiliation_string":"School of Software, BNRist, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051149140","display_name":"Sicheng Zhao","orcid":"https://orcid.org/0000-0001-5843-6411"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sicheng Zhao","raw_affiliation_strings":["BNRist, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5843-6411","affiliations":[{"raw_affiliation_string":"BNRist, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yifeng Zhang","orcid":"https://orcid.org/0009-0000-5023-9288"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifeng Zhang","raw_affiliation_strings":["jd.com, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0000-5023-9288","affiliations":[{"raw_affiliation_string":"jd.com, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100334231","display_name":"Hui Chen","orcid":"https://orcid.org/0000-0003-4180-5801"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Chen","raw_affiliation_strings":["BNRist, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4180-5801","affiliations":[{"raw_affiliation_string":"BNRist, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jundong Zhou","orcid":"https://orcid.org/0009-0005-4043-8693"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jundong Zhou","raw_affiliation_strings":["School of Software, BNRist, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0005-4043-8693","affiliations":[{"raw_affiliation_string":"School of Software, BNRist, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054904119","display_name":"Pengzhang Liu","orcid":"https://orcid.org/0000-0002-6031-5245"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengzhang Liu","raw_affiliation_strings":["jd.com, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6031-5245","affiliations":[{"raw_affiliation_string":"jd.com, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033318669","display_name":"Yongjun Bao","orcid":"https://orcid.org/0000-0002-7816-0587"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongjun Bao","raw_affiliation_strings":["jd.com, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7816-0587","affiliations":[{"raw_affiliation_string":"jd.com, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057732142","display_name":"Guiguang Ding","orcid":"https://orcid.org/0000-0003-0137-9975"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guiguang Ding","raw_affiliation_strings":["School of Software, BNRist, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0137-9975","affiliations":[{"raw_affiliation_string":"School of Software, BNRist, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4832","last_page":"4840"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9970999956130981,"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":0.9970999956130981,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9832000136375427,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.9667999744415283,"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/diagonal","display_name":"Diagonal","score":0.65196293592453},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6473809480667114},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.6383367776870728},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44479674100875854},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22135627269744873},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.2169252634048462}],"concepts":[{"id":"https://openalex.org/C130367717","wikidata":"https://www.wikidata.org/wiki/Q189791","display_name":"Diagonal","level":2,"score":0.65196293592453},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6473809480667114},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6383367776870728},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44479674100875854},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22135627269744873},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.2169252634048462},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3664647.3680793","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3680793","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":32,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W2007972815","https://openalex.org/W2031489346","https://openalex.org/W2108598243","https://openalex.org/W2143890915","https://openalex.org/W2194775991","https://openalex.org/W2410641892","https://openalex.org/W2554692997","https://openalex.org/W2612690371","https://openalex.org/W2618530766","https://openalex.org/W2768824252","https://openalex.org/W2932399282","https://openalex.org/W2969792713","https://openalex.org/W2982112268","https://openalex.org/W3034689791","https://openalex.org/W3087020912","https://openalex.org/W3089555680","https://openalex.org/W3092779813","https://openalex.org/W3095707208","https://openalex.org/W3120129399","https://openalex.org/W3165691894","https://openalex.org/W3166297290","https://openalex.org/W3168547821","https://openalex.org/W3207012649","https://openalex.org/W3216083537","https://openalex.org/W4214673031","https://openalex.org/W4225930680","https://openalex.org/W4226342448","https://openalex.org/W4304014049","https://openalex.org/W4386076454","https://openalex.org/W4387969338","https://openalex.org/W4402917176"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2135584473","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890"],"abstract_inverted_index":{"Collecting":[0],"large-scale":[1],"multi-label":[2],"data":[3],"with":[4,67,139,176],"full":[5],"labels":[6,23,55,70,119,129,175],"is":[7,181],"difficult":[8,73],"for":[9],"real-world":[10],"scenarios.":[11],"Many":[12],"existing":[13],"studies":[14],"have":[15],"tried":[16],"to":[17,46,53,88,132,171,183,194],"address":[18],"the":[19,29,33,39,64,69,90,140,161,173,185,196],"issue":[20,141],"of":[21,118,126,142,160,187,198],"missing":[22,144],"caused":[24],"by":[25],"annotation":[26,34,41],"but":[27],"ignored":[28],"difficulties":[30],"encountered":[31],"during":[32],"process.":[35],"We":[36],"find":[37],"that":[38,121,153],"high":[40],"workload":[42,91],"can":[43,98,111],"be":[44,99,133],"attributed":[45],"two":[47],"reasons:":[48],"(1)":[49],"Annotators":[50],"are":[51,130,192],"required":[52,131],"identify":[54],"on":[56,92,105,114,201],"widely":[57,203],"varying":[58],"visual":[59],"concepts.":[60],"(2)":[61],"Exhaustively":[62],"annotating":[63],"entire":[65],"dataset":[66],"all":[68],"becomes":[71],"notably":[72],"and":[74,107],"time-consuming.":[75],"In":[76,163],"this":[77],"paper,":[78],"we":[79,146,165],"propose":[80,166],"a":[81,123,148],"new":[82],"setting,":[83],"i.e.":[84],"block":[85],"diagonal":[86],"labels,":[87,145],"reduce":[89],"both":[93],"sides.":[94],"The":[95],"numerous":[96],"categories":[97],"divided":[100],"into":[101],"different":[102],"subsets":[103],"based":[104],"semantics":[106],"relevance.":[108],"Each":[109],"annotator":[110],"only":[112,122],"focus":[113],"its":[115],"own":[116],"subset":[117],"so":[120],"small":[124],"set":[125],"highly":[127],"relevant":[128],"annotated":[134],"per":[135],"image.":[136],"To":[137],"deal":[138],"such":[143],"introduce":[147],"simple":[149],"yet":[150],"effective":[151],"method":[152,170,200],"does":[154],"not":[155],"require":[156],"any":[157],"prior":[158],"knowledge":[159],"dataset.":[162],"practice,":[164],"an":[167],"Adaptive":[168],"Pseudo-Labeling":[169],"predict":[172],"unknown":[174],"less":[177],"noise.":[178],"Formal":[179],"analysis":[180],"conducted":[182,193],"evaluate":[184],"superiority":[186],"our":[188,199],"setting.":[189],"Extensive":[190],"experiments":[191],"verify":[195],"effectiveness":[197],"multiple":[202],"used":[204],"benchmarks.":[205]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
