{"id":"https://openalex.org/W4414942594","doi":"https://doi.org/10.1109/iccv51701.2025.00272","title":"Intervening in Black Box: Concept Bottleneck Model for Enhancing Human Neural Network Mutual Understanding","display_name":"Intervening in Black Box: Concept Bottleneck Model for Enhancing Human Neural Network Mutual Understanding","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4414942594","doi":"https://doi.org/10.1109/iccv51701.2025.00272"},"language":"en","primary_location":{"id":"doi:10.1109/iccv51701.2025.00272","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.00272","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2506.22803","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102604321","display_name":"Nuoye Xiong","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nuoye Xiong","raw_affiliation_strings":["Xidian University,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058247970","display_name":"Anqi Dong","orcid":"https://orcid.org/0000-0002-0365-0733"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Anqi Dong","raw_affiliation_strings":["KTH Royal Institute of Technology,Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology,Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100387096","display_name":"Ning Wang","orcid":"https://orcid.org/0000-0001-8903-8790"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Wang","raw_affiliation_strings":["Xidian University,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Cong Hua","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cong Hua","raw_affiliation_strings":["Xidian University,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028794275","display_name":"Guangming Zhu","orcid":"https://orcid.org/0000-0003-3214-4095"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangming Zhu","raw_affiliation_strings":["Xidian University,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019435534","display_name":"Lin Mei","orcid":null},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Mei","raw_affiliation_strings":["Donghai Laboratory,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Donghai Laboratory,China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103197309","display_name":"Peiyi Shen","orcid":"https://orcid.org/0000-0002-3527-7232"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peiyi Shen","raw_affiliation_strings":["Xidian University,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100425304","display_name":"Liang Zhang","orcid":"https://orcid.org/0009-0002-0017-2569"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Zhang","raw_affiliation_strings":["Xidian University,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"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":"2836","last_page":"2845"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.8299999833106995,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.8299999833106995,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.7749000191688538,"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/T14280","display_name":"Big Data Technologies and Applications","score":0.7324000000953674,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.9732999801635742},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.867900013923645},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49320000410079956},{"id":"https://openalex.org/keywords/black-box","display_name":"Black box","score":0.48399999737739563},{"id":"https://openalex.org/keywords/conceptual-model","display_name":"Conceptual model","score":0.39879998564720154},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.36469998955726624},{"id":"https://openalex.org/keywords/network-model","display_name":"Network model","score":0.3474999964237213},{"id":"https://openalex.org/keywords/information-bottleneck-method","display_name":"Information bottleneck method","score":0.34630000591278076}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9732999801635742},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.867900013923645},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7120000123977661},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6517999768257141},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5681999921798706},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49320000410079956},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.48399999737739563},{"id":"https://openalex.org/C13606891","wikidata":"https://www.wikidata.org/wiki/Q2623243","display_name":"Conceptual model","level":2,"score":0.39879998564720154},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.36469998955726624},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.3474999964237213},{"id":"https://openalex.org/C60008888","wikidata":"https://www.wikidata.org/wiki/Q6031013","display_name":"Information bottleneck method","level":3,"score":0.34630000591278076},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3163999915122986},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27970001101493835},{"id":"https://openalex.org/C8521452","wikidata":"https://www.wikidata.org/wiki/Q203790","display_name":"Connectionism","level":3,"score":0.26919999718666077},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.2556999921798706}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/iccv51701.2025.00272","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.00272","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2506.22803","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.22803","pdf_url":"https://arxiv.org/pdf/2506.22803","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2506.22803","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2506.22803","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":"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":"pmh:oai:arXiv.org:2506.22803","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.22803","pdf_url":"https://arxiv.org/pdf/2506.22803","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2542735305","display_name":null,"funder_award_id":"24511106900","funder_id":"https://openalex.org/F4320321885","funder_display_name":"Science and Technology Commission of Shanghai Municipality"},{"id":"https://openalex.org/G5485699470","display_name":null,"funder_award_id":"2024JCJCQN-66","funder_id":"https://openalex.org/F4320322666","funder_display_name":"Natural Science Foundation of Shanxi Province"},{"id":"https://openalex.org/G644447412","display_name":null,"funder_award_id":"62072358,62072352","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"},{"id":"https://openalex.org/F4320321885","display_name":"Science and Technology Commission of Shanghai Municipality","ror":"https://ror.org/03kt66j61"},{"id":"https://openalex.org/F4320322666","display_name":"Natural Science Foundation of Shanxi Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,138],"deep":[3],"learning":[4],"have":[5],"led":[6],"to":[7,24,73],"increasingly":[8],"complex":[9],"models":[10,119],"with":[11],"deeper":[12],"layers":[13],"and":[14,19,77,86,109,117,125,134],"more":[15],"parameters,":[16],"reducing":[17],"interpretability":[18,108],"making":[20],"their":[21],"decisions":[22],"harder":[23],"understand.":[25],"While":[26],"many":[27],"methods":[28],"explain":[29],"black-box":[30,75,104],"reasoning,":[31],"most":[32],"lack":[33],"effective":[34],"interventions":[35],"or":[36],"only":[37],"operate":[38],"at":[39],"sample-level":[40],"without":[41],"modifying":[42],"the":[43,51,64,103],"model":[44],"itself.":[45],"To":[46],"address":[47],"this,":[48],"we":[49],"propose":[50],"Concept":[52,65],"Bottleneck":[53,66],"Model":[54,67],"for":[55],"Enhancing":[56],"Human-Neural":[57],"Network":[58],"Mutual":[59],"Understanding":[60],"(CBM-HNMU).":[61],"CBM-HNMU":[62,113],"leverages":[63],"(CBM)":[68],"as":[69],"an":[70],"interpretable":[71],"framework":[72],"approximate":[74],"reasoning":[76],"communicate":[78],"conceptual":[79],"understanding.":[80],"Detrimental":[81],"concepts":[82],"are":[83],"automatically":[84],"identified":[85],"refined":[87],"(removed/replaced)":[88],"based":[89],"on":[90,114],"global":[91],"gradient":[92],"contributions.":[93],"The":[94],"modified":[95],"CBM":[96],"then":[97],"distills":[98],"corrected":[99],"knowledge":[100],"back":[101],"into":[102],"model,":[105],"enhancing":[106],"both":[107],"accuracy.":[110],"We":[111],"evaluate":[112],"various":[115],"CNN":[116],"transformer-based":[118],"across":[120,141],"Flower-102,":[121],"CIFAR-10,":[122],"CIFAR-100,":[123],"FGVC-Aircraft,":[124],"CUB-200,":[126],"achieving":[127],"a":[128,135],"maximum":[129,136],"accuracy":[130,140],"improvement":[131],"of":[132],"2.64%":[133],"increase":[137],"average":[139],"1.03%.":[142],"Source":[143],"code":[144],"is":[145],"available":[146],"at:":[147],"https://github.com/XiGuaBo/CBM-HNMU.":[148]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}