{"id":"https://openalex.org/W4415539131","doi":"https://doi.org/10.1145/3746027.3755193","title":"Clustering-Based Tail-class Mitigation for New-class Discovery","display_name":"Clustering-Based Tail-class Mitigation for New-class Discovery","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4415539131","doi":"https://doi.org/10.1145/3746027.3755193"},"language":null,"primary_location":{"id":"doi:10.1145/3746027.3755193","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd 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":"https://openalex.org/A5111350322","display_name":"Zelei Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zelei Wu","raw_affiliation_strings":["Ningbo University, Ningbo, China"],"affiliations":[{"raw_affiliation_string":"Ningbo University, Ningbo, China","institution_ids":["https://openalex.org/I109935558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078783784","display_name":"Xulun Ye","orcid":"https://orcid.org/0000-0003-0353-4748"},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xulun Ye","raw_affiliation_strings":["Ningbo University, Ningbo, China"],"affiliations":[{"raw_affiliation_string":"Ningbo University, Ningbo, China","institution_ids":["https://openalex.org/I109935558"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066282713","display_name":"Jieyu Zhao","orcid":"https://orcid.org/0000-0002-1013-557X"},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jieyu Zhao","raw_affiliation_strings":["Ningbo University, Ningbo, China"],"affiliations":[{"raw_affiliation_string":"Ningbo University, Ningbo, China","institution_ids":["https://openalex.org/I109935558"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5111350322"],"corresponding_institution_ids":["https://openalex.org/I109935558"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16014709,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3779","last_page":"3787"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","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"}},"topics":[{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9864000082015991,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9815000295639038,"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/class","display_name":"Class (philosophy)","score":0.5817999839782715},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5540000200271606},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5080000162124634},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4982999861240387},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4828999936580658},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.47269999980926514}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6600000262260437},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5817999839782715},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5659999847412109},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5540000200271606},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5080000162124634},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5060999989509583},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4982999861240387},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4828999936580658},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.47269999980926514},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4449000060558319},{"id":"https://openalex.org/C2780233690","wikidata":"https://www.wikidata.org/wiki/Q535347","display_name":"Transparency (behavior)","level":2,"score":0.3709999918937683},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.35740000009536743},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.33070001006126404},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2782000005245209}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746027.3755193","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5940647175","display_name":null,"funder_award_id":"62471266, 62006131, 62071260","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W3087124270","https://openalex.org/W3110446398","https://openalex.org/W3195139324","https://openalex.org/W4385767953","https://openalex.org/W4386065406","https://openalex.org/W4402660155","https://openalex.org/W4402753975","https://openalex.org/W4404956335","https://openalex.org/W4408710880"],"related_works":[],"abstract_inverted_index":{"Open-world":[0],"semi-supervised":[1,6],"learning":[2,7],"(OWSSL)":[3],"extends":[4],"traditional":[5],"to":[8,48,73,91],"open-world":[9,103,107],"scenarios":[10,105],"by":[11],"identifying":[12],"novel":[13,54,93],"categories":[14],"in":[15,102,106,117],"unlabeled":[16],"data,":[17],"thereby":[18],"enhancing":[19],"the":[20,59,68,129],"model's":[21],"generalization":[22],"capability.":[23],"However,":[24],"existing":[25],"OWSSL":[26],"datasets":[27],"typically":[28],"assume":[29],"a":[30,80,87],"balanced":[31],"class":[32,76,122],"distribution,":[33],"whereas":[34],"real-world":[35],"applications":[36],"often":[37],"exhibit":[38],"highly":[39],"imbalanced":[40],"distributions.":[41],"This":[42,56],"imbalance":[43],"makes":[44],"it":[45],"particularly":[46],"challenging":[47],"learn":[49],"tail":[50,75,114],"classes":[51],"and":[52,62,78,121,127],"discover":[53],"categories.":[55],"paper":[57],"introduces":[58],"Class-Balanced":[60],"Representation":[61],"Recognition":[63],"Framework":[64],"(CBTM-NCD),":[65],"which":[66],"uses":[67],"Variational":[69],"Dirichlet":[70],"Process":[71],"(VDP)":[72],"improve":[74],"features":[77],"includes":[79],"generative":[81],"data":[82],"balancing":[83],"strategy.Additionally,":[84],"CBTM-NCD":[85],"adopts":[86],"two-stage":[88],"optimization":[89],"strategy":[90],"identify":[92],"category":[94],"samples,":[95],"effectively":[96],"tackling":[97],"three":[98],"major":[99],"challenges":[100],"prevalent":[101],"long-tailed":[104,108],"distributions:":[109],"insufficient":[110],"feature":[111],"representation":[112],"of":[113],"classes,":[115],"difficulty":[116],"discovering":[118],"unknown":[119],"categories,":[120],"distribution":[123],"imbalance.To":[124],"enhance":[125],"transparency":[126],"reproducibility,":[128],"code":[130],"is":[131],"available":[132],"at":[133],"https://github.com/wuzelei123/CBTM-NCD.":[134]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-25T00:00:00"}
