{"id":"https://openalex.org/W7161255996","doi":"https://doi.org/10.48550/arxiv.2605.14808","title":"SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track","display_name":"SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track","publication_year":2026,"publication_date":"2026-05-14","ids":{"openalex":"https://openalex.org/W7161255996","doi":"https://doi.org/10.48550/arxiv.2605.14808"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.14808","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14808","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2605.14808","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092740240","display_name":"Lukas Roming","orcid":"https://orcid.org/0009-0006-1330-3117"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roming, Lukas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136219613","display_name":"Felix Lehnerer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lehnerer, Felix","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123617378","display_name":"Jonas V. Funk","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Funk, Jonas V.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136200738","display_name":"Andreas Michel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michel, Andreas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136188300","display_name":"Georg Maier","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maier, Georg","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025271723","display_name":"Thomas L\u00e4ngle","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"L\u00e4ngle, Thomas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5073930300","display_name":"J\u00fcrgen Beyerer","orcid":"https://orcid.org/0000-0003-3556-7181"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Beyerer, J\u00fcrgen","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":0.9593999981880188,"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":0.9593999981880188,"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":0.009600000455975533,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.003599999938160181,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6000000238418579},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5494999885559082},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5202999711036682},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4050000011920929},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.38179999589920044},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.364300012588501},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.33880001306533813},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.3158000111579895},{"id":"https://openalex.org/keywords/industrial-production","display_name":"Industrial production","score":0.2912999987602234}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.76419997215271},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6000000238418579},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5680999755859375},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5494999885559082},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5202999711036682},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4050000011920929},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39590001106262207},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.38179999589920044},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.364300012588501},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35850000381469727},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34689998626708984},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.33880001306533813},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3158000111579895},{"id":"https://openalex.org/C82753439","wikidata":"https://www.wikidata.org/wiki/Q1419090","display_name":"Industrial production","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2694999873638153},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.2612000107765198},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.25870001316070557},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.25769999623298645},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.14808","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14808","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":"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":"doi:10.48550/arxiv.2605.14808","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14808","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6641204953193665}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Visual":[0],"anomaly":[1,73,128],"detection":[2,74],"(AD)":[3],"for":[4,111,122,162],"industrial":[5,163],"inspection":[6],"is":[7,55,208],"a":[8,69,100,144],"highly":[9],"relevant":[10],"task":[11],"in":[12,30],"modern":[13],"production":[14],"environments.":[15],"The":[16],"problem":[17],"becomes":[18],"particularly":[19],"challenging":[20],"when":[21],"training":[22],"and":[23,52,71,118,124,147,168,186,192,203],"deployment":[24],"data":[25,116],"differ":[26],"due":[27],"to":[28,90],"changes":[29,170],"acquisition":[31],"conditions":[32],"during":[33],"production.":[34],"In":[35],"the":[36,58,78,115,158],"VAND":[37],"4.0":[38],"Industrial":[39],"Track,":[40],"models":[41],"must":[42,171],"remain":[43],"robust":[44],"under":[45,93],"distribution":[46,94],"shifts":[47],"such":[48],"as":[49],"varying":[50],"illumination":[51],"their":[53],"performance":[54],"assessed":[56],"on":[57,77,134,188],"MVTec":[59,196],"AD":[60,197],"2":[61,198],"dataset.":[62],"To":[63],"address":[64],"this":[65],"setting,":[66],"we":[67],"propose":[68],"training-free":[70],"class-agnostic":[72],"pipeline":[75],"based":[76],"work":[79],"of":[80,114,183,195],"SuperAD.":[81],"Our":[82],"approach":[83,159],"improves":[84],"generalization":[85],"through":[86],"several":[87],"modifications":[88],"designed":[89],"enhance":[91],"robustness":[92],"shifts.":[95],"These":[96],"adaptations":[97],"include":[98],"using":[99],"DINOv3":[101],"backbone,":[102],"overlapping":[103],"patch-wise":[104],"processing,":[105],"intensity-based":[106],"augmentations,":[107],"improved":[108],"memory-bank":[109],"subsampling":[110],"better":[112],"coverage":[113],"distribution,":[117],"iterative":[119],"morphological":[120],"closing":[121],"cleaner":[123],"more":[125],"spatially":[126],"consistent":[127],"maps.":[129],"Unlike":[130],"methods":[131],"that":[132],"rely":[133],"class-specific":[135],"architectures":[136],"or":[137],"per-class":[138],"hyperparameter":[139,150],"tuning,":[140],"our":[141],"method":[142],"uses":[143],"single":[145],"architecture":[146],"one":[148],"shared":[149],"configuration":[151],"across":[152],"all":[153],"object":[154],"classes.":[155],"This":[156],"makes":[157],"well":[160],"suited":[161],"deployment,":[164],"where":[165],"product":[166],"variants":[167],"appearance":[169],"be":[172],"handled":[173],"with":[174],"minimal":[175],"adaptation":[176],"effort.":[177],"We":[178],"achieve":[179],"segmentation":[180],"F1":[181],"scores":[182],"$62.61\\%$,":[184],"$57.42\\%$,":[185],"$54.35\\%$":[187],"test":[189],"public,":[190],"private,":[191],"private":[193],"mixed":[194],"respectively,":[199],"thereby":[200],"outperforming":[201],"SuperAD":[202],"other":[204],"state-of-the-art":[205],"methods.":[206],"Code":[207],"available":[209],"at":[210],"https://github.com/LukasRoom/SuperADD.":[211]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-16T00:00:00"}