{"id":"https://openalex.org/W7159659106","doi":"https://doi.org/10.48550/arxiv.2604.27596","title":"SECOS: Semantic Capture for Rigorous Classification in Open-World Semi-Supervised Learning","display_name":"SECOS: Semantic Capture for Rigorous Classification in Open-World Semi-Supervised Learning","publication_year":2026,"publication_date":"2026-04-30","ids":{"openalex":"https://openalex.org/W7159659106","doi":"https://doi.org/10.48550/arxiv.2604.27596"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.27596","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27596","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.27596","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108995896","display_name":"Hezhao Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Hezhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134982718","display_name":"Jiacheng Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Jiacheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016259963","display_name":"Junlong Gao","orcid":"https://orcid.org/0000-0002-8734-1021"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Junlong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134974239","display_name":"Mengke Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Mengke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134942422","display_name":"Yiqun Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yiqun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134952448","display_name":"Shreyank N Gowda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gowda, Shreyank N","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134991432","display_name":"Yang Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9248999953269958,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9248999953269958,"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":0.014000000432133675,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.013199999928474426,"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/set","display_name":"Set (abstract data type)","score":0.6065000295639038},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5971999764442444},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5311999917030334},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4812999963760376},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.3955000042915344},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.39169999957084656},{"id":"https://openalex.org/keywords/semantic-data-model","display_name":"Semantic data model","score":0.37959998846054077},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.3546999990940094}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7867000102996826},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6098999977111816},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6065000295639038},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5971999764442444},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5311999917030334},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4812999963760376},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4413999915122986},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4115000069141388},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3955000042915344},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.39169999957084656},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.37959998846054077},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3546999990940094},{"id":"https://openalex.org/C2778493491","wikidata":"https://www.wikidata.org/wiki/Q7449072","display_name":"Semantic matching","level":3,"score":0.3366999924182892},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.30559998750686646},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C511149849","wikidata":"https://www.wikidata.org/wiki/Q7449051","display_name":"Semantic computing","level":3,"score":0.25859999656677246},{"id":"https://openalex.org/C197914299","wikidata":"https://www.wikidata.org/wiki/Q18650","display_name":"Semantic memory","level":3,"score":0.2549999952316284},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.27596","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27596","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.27596","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27596","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.6720258593559265,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0,20],"open-world":[1],"semi-supervised":[2],"learning":[3,94],"(OWSSL),":[4],"a":[5,40],"model":[6],"learns":[7],"from":[8,39,101],"labeled":[9],"data":[10,13],"and":[11,17,61,120,129],"unlabeled":[12],"containing":[14],"both":[15,127],"known":[16,128],"novel":[18,54,130,138],"classes.":[19,139],"practical":[21,111],"OWSSL":[22,47,112,147],"applications,":[23],"models":[24],"are":[25,56,149],"expected":[26],"to":[27,50,66,80,118,164],"perform":[28],"rigorous":[29],"classification":[30],"by":[31,162],"directly":[32,97],"selecting":[33],"the":[34,102,108,152],"most":[35],"semantically":[36],"relevant":[37],"label":[38],"candidate":[41,81,103],"set":[42,104],"for":[43,91,126,136],"each":[44],"sample.":[45],"Existing":[46],"methods":[48,63,148],"fail":[49],"achieve":[51],"this":[52],"because":[53],"samples":[55],"trained":[57],"without":[58,105,166],"explicit":[59,133],"supervision,":[60],"these":[62],"lack":[64],"mechanisms":[65],"extract":[67,119],"latent":[68],"semantic":[69,78,122],"information,":[70],"resulting":[71],"in":[72],"predicted":[73],"labels":[74,100],"that":[75,143],"have":[76],"no":[77],"correspondence":[79],"textual":[82,99],"labels.":[83],"To":[84],"address":[85],"this,":[86],"we":[87],"introduce":[88],"SEmantic":[89],"Capture":[90],"Open-world":[92],"Semi-supervised":[93],"(SECOS),":[95],"which":[96],"predicts":[98],"post-processing,":[106],"meeting":[107],"requirements":[109],"of":[110],"applications.":[113],"SECOS":[114,158],"leverages":[115],"external":[116],"knowledge":[117],"align":[121],"representations":[123],"across":[124],"modalities":[125],"classes,":[131],"providing":[132],"supervisory":[134],"signals":[135],"training":[137],"Extensive":[140],"experiments":[141],"demonstrate":[142],"even":[144],"when":[145],"existing":[146],"evaluated":[150],"under":[151],"more":[153],"lenient":[154],"post-hoc":[155],"matching":[156],"setting,":[157],"still":[159],"surpasses":[160],"them":[161],"up":[163],"5.4\\%":[165],"such":[167],"assistance,":[168],"highlighting":[169],"its":[170],"superior":[171],"effectiveness.":[172],"Code":[173],"is":[174],"available":[175],"at":[176],"https://github.com/ganchi-huanggua/OSSL-Classification.":[177]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-02T00:00:00"}