{"id":"https://openalex.org/W7160233962","doi":"https://doi.org/10.1109/wacv61042.2026.00505","title":"Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects","display_name":"Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7160233962","doi":"https://doi.org/10.1109/wacv61042.2026.00505"},"language":null,"primary_location":{"id":"doi:10.1109/wacv61042.2026.00505","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00505","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","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/A5135416650","display_name":"Yixin Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yixin Zhang","raw_affiliation_strings":["Duke University,Department of Electrical and Computer Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Duke University,Department of Electrical and Computer Engineering","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067091221","display_name":"Nicholas Konz","orcid":"https://orcid.org/0000-0003-0230-1598"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nicholas Konz","raw_affiliation_strings":["Duke University,Department of Electrical and Computer Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Duke University,Department of Electrical and Computer Engineering","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135336348","display_name":"Kevin Kramer","orcid":null},"institutions":[{"id":"https://openalex.org/I4210117012","display_name":"Health Solutions (Sweden)","ror":"https://ror.org/02tdmp521","country_code":"SE","type":"company","lineage":["https://openalex.org/I4210117012"]},{"id":"https://openalex.org/I4210144997","display_name":"Minnesota Department of Health","ror":"https://ror.org/04g43x563","country_code":"US","type":"government","lineage":["https://openalex.org/I4210144997"]}],"countries":["SE","US"],"is_corresponding":false,"raw_author_name":"Kevin Kramer","raw_affiliation_strings":["Minnesota Health Solutions"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Minnesota Health Solutions","institution_ids":["https://openalex.org/I4210117012","https://openalex.org/I4210144997"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001300575","display_name":"Maciej A. Mazurowski","orcid":"https://orcid.org/0000-0003-4202-8602"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maciej A. Mazurowski","raw_affiliation_strings":["Duke University,Department of Electrical and Computer Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Duke University,Department of Electrical and Computer Engineering","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":17.6924,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.9908288,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"5205","last_page":"5215"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.12620000541210175,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.12620000541210175,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.11779999732971191,"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/T11596","display_name":"Constraint Satisfaction and Optimization","score":0.07360000163316727,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.5842000246047974},{"id":"https://openalex.org/keywords/market-segmentation","display_name":"Market segmentation","score":0.4415999948978424},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.3986999988555908},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.3634999990463257},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3481000065803528}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5843999981880188},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5842000246047974},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.550000011920929},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.4415999948978424},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4401000142097473},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3986999988555908},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.3634999990463257},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3481000065803528},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32670000195503235},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26750001311302185},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.2615000009536743}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv61042.2026.00505","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00505","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320338440","display_name":"HORIZON EUROPE Health","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1849277567","https://openalex.org/W1965202763","https://openalex.org/W2015159529","https://openalex.org/W2078483536","https://openalex.org/W2101234009","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2343204383","https://openalex.org/W2475287302","https://openalex.org/W2804199516","https://openalex.org/W2889985731","https://openalex.org/W2903867357","https://openalex.org/W2963150697","https://openalex.org/W3035422918","https://openalex.org/W3094502228","https://openalex.org/W3176288137","https://openalex.org/W4239181501","https://openalex.org/W4247110653","https://openalex.org/W4309729079","https://openalex.org/W4312964941","https://openalex.org/W4379141755","https://openalex.org/W4385481295","https://openalex.org/W4386071839","https://openalex.org/W4387369035","https://openalex.org/W4389430914","https://openalex.org/W4390190100","https://openalex.org/W4390873799","https://openalex.org/W4390874575","https://openalex.org/W4391021462","https://openalex.org/W4391109864","https://openalex.org/W4391306282","https://openalex.org/W4393154423","https://openalex.org/W4394597311","https://openalex.org/W4394758452","https://openalex.org/W4401072727","https://openalex.org/W4401793463","https://openalex.org/W4402816732","https://openalex.org/W4402961794","https://openalex.org/W4404722477","https://openalex.org/W4407005368","https://openalex.org/W4409245106","https://openalex.org/W4410213435","https://openalex.org/W7133191568","https://openalex.org/W7133193691"],"related_works":[],"abstract_inverted_index":{"Image":[0],"segmentation":[1,16,149],"foundation":[2],"models":[3],"(SFMs)":[4],"like":[5],"Segment":[6],"Anything":[7],"Model":[8],"(SAM)":[9],"have":[10],"achieved":[11],"impressive":[12],"zero-shot":[13],"and":[14,35,68,76],"interactive":[15],"across":[17],"diverse":[18],"domains.":[19],"However,":[20],"they":[21],"struggle":[22],"to":[23,98,120],"segment":[24],"objects":[25,112],"with":[26,31,91],"certain":[27],"structures,":[28,143],"particularly":[29],"those":[30],"dense,":[32],"tree-like":[33],"morphology":[34],"low":[36],"textural":[37,69],"contrast":[38],"from":[39,113],"their":[40,148],"surroundings.":[41],"These":[42],"failure":[43],"modes":[44],"are":[45],"crucial":[46],"for":[47,135],"understanding":[48],"the":[49,131,137],"limitations":[50],"of":[51,139],"SFMs":[52,99,140],"in":[53,107],"real-world":[54,77],"applications.":[55],"To":[56],"systematically":[57],"study":[58,129],"this":[59,122],"issue,":[60,123],"we":[61,79],"introduce":[62],"interpretable":[63,145],"metrics":[64],"quantifying":[65],"object":[66],"tree-likeness":[67],"separability.":[70],"On":[71],"carefully":[72],"controlled":[73],"synthetic":[74],"experiments":[75],"datasets,":[78],"show":[80],"that":[81],"SFM":[82],"performance":[83],"(e.g.,":[84],"SAM,":[85],"SAM":[86],"2,":[87],"HQ-SAM)":[88],"noticeably":[89],"correlates":[90],"these":[92,96],"factors.":[93],"We":[94],"attribute":[95],"failures":[97],"misinterpreting":[100],"local":[101],"structure":[102],"as":[103],"global":[104],"texture,":[105],"resulting":[106],"over-segmentation":[108],"or":[109],"difficulty":[110],"distinguishing":[111],"similar":[114],"backgrounds.":[115],"Notably,":[116],"targeted":[117],"fine-tuning":[118],"fails":[119],"resolve":[121],"indicating":[124],"a":[125],"fundamental":[126],"limitation.":[127],"Our":[128],"provides":[130],"first":[132],"quantitative":[133],"framework":[134],"modeling":[136],"behavior":[138],"on":[141],"challenging":[142],"offering":[144],"insights":[146],"into":[147],"capabilities.":[150],"<sup":[151],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[152],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[153]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-06T00:00:00"}
