{"id":"https://openalex.org/W7133338575","doi":"https://doi.org/10.48550/arxiv.2603.01305","title":"AG-VAS: Anchor-Guided Zero-Shot Visual Anomaly Segmentation with Large Multimodal Models","display_name":"AG-VAS: Anchor-Guided Zero-Shot Visual Anomaly Segmentation with Large Multimodal Models","publication_year":2026,"publication_date":"2026-03-01","ids":{"openalex":"https://openalex.org/W7133338575","doi":"https://doi.org/10.48550/arxiv.2603.01305"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01305","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01305","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":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.2603.01305","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090460556","display_name":"Zhen Qu","orcid":"https://orcid.org/0009-0000-2173-612X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qu, Zhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127950985","display_name":"Xian Tao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tao, Xian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039389811","display_name":"Xiaoyi Bao","orcid":"https://orcid.org/0000-0002-5525-0694"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bao, Xiaoyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023015242","display_name":"Dingrong Wang","orcid":"https://orcid.org/0009-0005-2407-2337"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Dingrong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127955073","display_name":"ShiChen Qu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qu, ShiChen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127874440","display_name":"Zhengtao Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhengtao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127963756","display_name":"Xingang Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xingang","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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9192000031471252,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9192000031471252,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.02449999935925007,"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":0.008299999870359898,"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/anomaly","display_name":"Anomaly (physics)","score":0.6416000127792358},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6367999911308289},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5935999751091003},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5164999961853027},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4772000014781952},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4519999921321869}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7149999737739563},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.6416000127792358},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6367999911308289},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6032999753952026},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5935999751091003},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5164999961853027},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4772000014781952},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4519999921321869},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4092999994754791},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.4068000018596649},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.40380001068115234},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3319999873638153},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.325300008058548},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2757999897003174}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01305","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01305","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":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.2603.01305","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01305","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.7210652232170105}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"multimodal":[1],"models":[2],"(LMMs)":[3],"exhibit":[4],"strong":[5],"task":[6],"generalization":[7],"capabilities,":[8],"offering":[9],"new":[10,66],"opportunities":[11],"for":[12,164],"zero-shot":[13,217],"visual":[14,36,105,150],"anomaly":[15,27,52,99,166,179],"segmentation":[16,21,86],"(ZSAS).":[17],"However,":[18],"existing":[19],"LMM-based":[20],"approaches":[22],"still":[23],"face":[24],"fundamental":[25],"limitations:":[26],"concepts":[28],"are":[29],"inherently":[30],"abstract":[31,98],"and":[32,38,46,80,113,126,187,193,205],"context-dependent,":[33],"lacking":[34],"stable":[35],"prototypes,":[37],"the":[39,70,121,196,216],"weak":[40],"alignment":[41],"between":[42,124],"high-level":[43],"semantic":[44,76,94,146,198],"embeddings":[45,147],"pixel-level":[47],"spatial":[48,188],"features":[49],"hinders":[50],"precise":[51,165],"localization.":[53,167],"To":[54,131],"address":[55],"these":[56],"challenges,":[57],"we":[58,136,170],"present":[59],"AG-VAS":[60,210],"(Anchor-Guided":[61],"Visual":[62],"Anomaly":[63],"Segmentation),":[64],"a":[65,83,138,173],"framework":[67],"that":[68,96,119,143,159,177,209],"expands":[69],"LMM":[71],"vocabulary":[72],"with":[73,148,153],"three":[74],"learnable":[75],"anchor":[77,95],"tokens-[SEG],":[78],"[NOR],":[79],"[ANO],":[81],"establishing":[82],"unified":[84],"anchor-guided":[85],"paradigm.":[87],"Specifically,":[88],"[SEG]":[89],"serves":[90],"as":[91,116],"an":[92,154],"absolute":[93],"translates":[97],"semantics":[100],"into":[101,181],"explicit,":[102],"spatially":[103],"grounded":[104],"entities":[106],"(e.g.,":[107],"holes":[108],"or":[109],"scratches),":[110],"while":[111],"[NOR]":[112],"[ANO]":[114],"act":[115],"relative":[117],"anchors":[118],"model":[120],"contextual":[122],"contrast":[123],"normal":[125],"abnormal":[127],"patterns":[128],"across":[129],"categories.":[130],"further":[132],"enhance":[133],"cross-modal":[134],"alignment,":[135],"introduce":[137],"Semantic-Pixel":[139],"Alignment":[140],"Module":[141],"(SPAM)":[142],"aligns":[144],"language-level":[145],"high-resolution":[149],"features,":[151],"along":[152],"Anchor-Guided":[155],"Mask":[156],"Decoder":[157],"(AGMD)":[158],"performs":[160],"anchor-conditioned":[161],"mask":[162],"prediction":[163],"In":[168],"addition,":[169],"curate":[171],"Anomaly-Instruct20K,":[172],"large-scale":[174],"instruction":[175],"dataset":[176],"organizes":[178],"knowledge":[180],"structured":[182],"descriptions":[183],"of":[184,195],"appearance,":[185],"shape,":[186],"attributes,":[189],"facilitating":[190],"effective":[191],"learning":[192],"integration":[194],"proposed":[197],"anchors.":[199],"Extensive":[200],"experiments":[201],"on":[202],"six":[203],"industrial":[204],"medical":[206],"benchmarks":[207],"demonstrate":[208],"achieves":[211],"consistent":[212],"state-of-the-art":[213],"performance":[214],"in":[215],"setting.":[218]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-04T00:00:00"}
