{"id":"https://openalex.org/W4416267322","doi":"https://doi.org/10.48550/arxiv.2503.20301","title":"Attribute-formed Class-specific Concept Space: Endowing Language Bottleneck Model with Better Interpretability and Scalability","display_name":"Attribute-formed Class-specific Concept Space: Endowing Language Bottleneck Model with Better Interpretability and Scalability","publication_year":2025,"publication_date":"2025-03-26","ids":{"openalex":"https://openalex.org/W4416267322","doi":"https://doi.org/10.48550/arxiv.2503.20301"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2503.20301","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2503.20301","pdf_url":"https://arxiv.org/pdf/2503.20301","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2503.20301","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035818958","display_name":"Jianyang Zhang","orcid":"https://orcid.org/0000-0001-5968-5997"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhang, Jianyang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Luo, Qianli","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Qianli","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012354366","display_name":"Guowu Yang","orcid":"https://orcid.org/0000-0002-5133-0320"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Guowu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016603628","display_name":"Wenjing Yang","orcid":"https://orcid.org/0000-0002-6997-0406"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Wenjing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033446854","display_name":"Weide Liu","orcid":"https://orcid.org/0000-0002-9855-4479"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Weide","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029912845","display_name":"Guosheng Lin","orcid":"https://orcid.org/0000-0002-0329-7458"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Guosheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5009594218","display_name":"Fengmao Lv","orcid":"https://orcid.org/0000-0003-1640-0992"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lv, Fengmao","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5035818958"],"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":0.5461999773979187,"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":0.5461999773979187,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.21050000190734863,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.043800000101327896,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/interpretability","display_name":"Interpretability","score":0.8155999779701233},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.761900007724762},{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.5986999869346619},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5863000154495239},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5188999772071838},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.44609999656677246},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.43880000710487366}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8155999779701233},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.761900007724762},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7407000064849854},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6330999732017517},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.5986999869346619},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5863000154495239},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5188999772071838},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5163999795913696},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.44609999656677246},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.43880000710487366},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3630000054836273},{"id":"https://openalex.org/C60008888","wikidata":"https://www.wikidata.org/wiki/Q6031013","display_name":"Information bottleneck method","level":3,"score":0.3127000033855438},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.27379998564720154},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2718000113964081},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2596000134944916}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2503.20301","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2503.20301","pdf_url":"https://arxiv.org/pdf/2503.20301","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2503.20301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2503.20301","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2503.20301","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2503.20301","pdf_url":"https://arxiv.org/pdf/2503.20301","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Language":[0,52],"Bottleneck":[1,53],"Models":[2],"(LBMs)":[3],"are":[4,66,198],"proposed":[5],"to":[6,32,41,126,130,141,149,163],"achieve":[7],"interpretable":[8],"image":[9],"recognition":[10],"by":[11,85],"classifying":[12,86],"images":[13],"based":[14,88],"on":[15,89,145,177],"textual":[16],"concept":[17,107,120,152,167,196],"bottlenecks.":[18],"However,":[19],"current":[20],"LBMs":[21],"simply":[22],"list":[23],"all":[24],"concepts":[25,58,65,92],"together":[26],"as":[27,115],"the":[28,33,50,60,80,90,98,106,118,156,184],"bottleneck":[29],"layer,":[30],"leading":[31],"spurious":[34,81],"cue":[35,82],"inference":[36,83],"problem":[37,84],"and":[38,159,172,187,194],"cannot":[39],"generalized":[40,125],"unseen":[42,127],"classes.":[43,73,128],"To":[44],"address":[45],"these":[46],"limitations,":[47],"we":[48,134,154],"propose":[49,135,155],"Attribute-formed":[51],"Model":[54],"(ALBM).":[55],"ALBM":[56,77],"organizes":[57],"in":[59],"attribute-formed":[61],"class-specific":[62],"space,":[63],"where":[64],"descriptions":[67],"of":[68,93,109,189],"specific":[69,72],"attributes":[70],"for":[71],"In":[74,96],"this":[75],"way,":[76],"can":[78,122],"avoid":[79,150],"solely":[87],"essential":[91],"each":[94],"class.":[95],"addition,":[97],"cross-class":[99],"unified":[100],"attribute":[101],"set":[102],"also":[103],"ensures":[104],"that":[105],"spaces":[108],"different":[110],"classes":[111],"have":[112],"strong":[113],"correlations,":[114],"a":[116,170],"result,":[117],"learned":[119],"classifier":[121],"be":[123],"easily":[124],"Moreover,":[129],"further":[131],"improve":[132],"interpretability,":[133,185],"Visual":[136],"Attribute":[137],"Prompt":[138],"Learning":[139],"(VAPL)":[140],"extract":[142],"visual":[143],"features":[144],"fine-grained":[146],"attributes.":[147],"Furthermore,":[148],"labor-intensive":[151],"annotation,":[153],"Description,":[157],"Summary,":[158],"Supplement":[160],"(DSS)":[161],"strategy":[162],"automatically":[164],"generate":[165],"high-quality":[166],"sets":[168,197],"with":[169],"complete":[171],"precise":[173],"attribute.":[174],"Extensive":[175],"experiments":[176],"9":[178],"widely":[179],"used":[180],"few-shot":[181],"benchmarks":[182],"demonstrate":[183],"transferability,":[186],"performance":[188],"our":[190],"approach.":[191],"The":[192],"code":[193],"collected":[195],"available":[199],"at":[200],"https://github.com/tiggers23/ALBM.":[201]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}