{"id":"https://openalex.org/W7162807855","doi":"https://doi.org/10.48550/arxiv.2605.29691","title":"Unsupervised Semantic Segmentation Facilitates Model Understanding","display_name":"Unsupervised Semantic Segmentation Facilitates Model Understanding","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162807855","doi":"https://doi.org/10.48550/arxiv.2605.29691"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.29691","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29691","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":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.2605.29691","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137316565","display_name":"Xiaoyan Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Xiaoyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052086768","display_name":"Lisa Mais","orcid":"https://orcid.org/0000-0002-9281-2668"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mais, Lisa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002413797","display_name":"Jannik Franzen","orcid":"https://orcid.org/0000-0002-0761-641X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Franzen, Jannik","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137322142","display_name":"Peter Hirsch","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hirsch, Peter","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137310478","display_name":"Nick Lechtenb\u00f6rger","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lechtenb\u00f6rger, Nick","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014208179","display_name":"Andreas Mardt","orcid":"https://orcid.org/0000-0002-7353-6063"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mardt, Andreas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5110722463","display_name":"Dagmar Kainm\u00fcller","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kainm\u00fcller, Dagmar","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":0.6549999713897705,"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":0.6549999713897705,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.125900000333786,"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":0.025100000202655792,"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/segmentation","display_name":"Segmentation","score":0.5866000056266785},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.5529000163078308},{"id":"https://openalex.org/keywords/locality","display_name":"Locality","score":0.49709999561309814},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4821000099182129},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.4652000069618225},{"id":"https://openalex.org/keywords/complement","display_name":"Complement (music)","score":0.45080000162124634},{"id":"https://openalex.org/keywords/protocol","display_name":"Protocol (science)","score":0.4293000102043152},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4244000017642975}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.786300003528595},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5866000056266785},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5787000060081482},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.5529000163078308},{"id":"https://openalex.org/C2779808786","wikidata":"https://www.wikidata.org/wiki/Q6664603","display_name":"Locality","level":2,"score":0.49709999561309814},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4821000099182129},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.4652000069618225},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.45080000162124634},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.4293000102043152},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4244000017642975},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4189999997615814},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.3589000105857849},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.34619998931884766},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3375999927520752},{"id":"https://openalex.org/C14669888","wikidata":"https://www.wikidata.org/wiki/Q4014850","display_name":"Creative visualization","level":3,"score":0.32030001282691956},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.30970001220703125},{"id":"https://openalex.org/C2779714256","wikidata":"https://www.wikidata.org/wiki/Q25305062","display_name":"Multiple Models","level":2,"score":0.3084000051021576},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.28349998593330383},{"id":"https://openalex.org/C172367668","wikidata":"https://www.wikidata.org/wiki/Q6504956","display_name":"Data visualization","level":3,"score":0.25429999828338623},{"id":"https://openalex.org/C91682802","wikidata":"https://www.wikidata.org/wiki/Q620538","display_name":"Multidimensional scaling","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.29691","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29691","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.29691","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29691","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Self-supervised":[0],"learning":[1,61],"(SSL)":[2],"has":[3,33,77],"produced":[4],"a":[5,16,23,29,84,112,117,162,206,257],"diverse":[6,163],"landscape":[7],"of":[8,19,26,31,37,45,71,165,202,208],"vision":[9],"transformers":[10],"(ViTs)":[11],"whose":[12],"pretrained":[13],"representations":[14],"support":[15],"wide":[17],"range":[18,207],"downstream":[20],"tasks.":[21],"Towards":[22],"better":[24],"understanding":[25,76,107,255],"these":[27,72],"models,":[28],"body":[30],"work":[32],"assessed":[34],"the":[35,43,69,226,233,241],"mechanics":[36],"their":[38,49],"self-attention":[39],"as":[40,42],"well":[41],"types":[44],"information":[46],"captured":[47],"across":[48,158,168],"representations,":[50,171],"revealing,":[51],"for":[52,111,256],"example,":[53],"stark":[54],"differences":[55],"between":[56,222],"models":[57,95,167],"trained":[58],"with":[59],"contrastive":[60],"(CL)":[62],"and":[63,109,119,170,184,220,225],"masked":[64],"image":[65],"modeling":[66],"(MIM).":[67],"However,":[68],"total":[70],"advances":[73],"on":[74,128,141,161,200],"model":[75,106,153,194,254],"to":[78,93,101,150,176,216,251],"date":[79],"not":[80],"yet":[81,134],"fully":[82],"permeated":[83],"larger":[85],"community,":[86,114],"where,":[87],"e.g.,":[88,188],"insights":[89,179,198],"that":[90,155],"are":[91,96],"specific":[92],"CL":[94],"still":[97],"at":[98],"times":[99],"generalized":[100],"MIM":[102],"models.":[103],"To":[104],"make":[105],"straightforward":[108],"intuitive":[110],"broad":[113,258],"we":[115,139],"propose":[116],"simple":[118],"easily":[120,151,204],"interpretable":[121],"visualization":[122],"protocol.":[123],"Our":[124,211,245],"protocol":[125,147,173,212,246],"is":[126,247],"based":[127],"visualizing":[129],"unsupervised":[130],"semantic":[131],"segmentation":[132,143],"results,":[133],"by":[135],"no":[136],"means":[137],"do":[138],"focus":[140],"top":[142,201],"performance.":[144],"Instead,":[145],"our":[146,172],"allows":[148,174,214],"us":[149,175,215],"convey":[152,219],"behavior":[154],"consistently":[156],"emerges":[157],"images.":[159],"Benchmarked":[160],"set":[164],"SSL":[166],"layers":[169],"gain":[177],"novel":[178,197],"into":[180],"distinct":[181,230],"positional":[182,223],"biases":[183],"scaling":[185],"behaviors,":[186],"including,":[187],"strong":[189],"boundary":[190],"artifacts":[191],"in":[192,240],"DINOv3-Large":[193],"tokens.":[195],"These":[196],"come":[199],"more":[203,237],"conveying":[205],"previous":[209],"findings.":[210],"further":[213,253],"clearly":[217],"visually":[218],"distinguish":[221],"effects":[224],"closely":[227],"related":[228],"but":[229],"locality":[231],"bias,":[232],"latter":[234],"being":[235],"much":[236],"extensively":[238],"studied":[239],"literature":[242],"so":[243],"far.":[244],"publicly":[248],"available,":[249],"serving":[250],"catalyze":[252],"community.":[259]},"counts_by_year":[],"updated_date":"2026-07-04T06:09:54.619538","created_date":"2026-05-30T00:00:00"}
