{"id":"https://openalex.org/W7154188110","doi":"https://doi.org/10.48550/arxiv.2604.08915","title":"Large-Scale Universal Defect Generation: Foundation Models and Datasets","display_name":"Large-Scale Universal Defect Generation: Foundation Models and Datasets","publication_year":2026,"publication_date":"2026-04-10","ids":{"openalex":"https://openalex.org/W7154188110","doi":"https://doi.org/10.48550/arxiv.2604.08915"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.08915","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08915","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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.08915","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101323076","display_name":"Yuanting Fan","orcid":"https://orcid.org/0009-0008-6507-666X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Fan, Yuanting","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133476503","display_name":"Jun Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133488254","display_name":"Bin-Bin Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Bin-Bin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133517262","display_name":"Xiaochen Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xiaochen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101373524","display_name":"Yuhuan Lin","orcid":"https://orcid.org/0000-0002-5117-5893"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Yuhuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032094506","display_name":"Zhewei Dai","orcid":"https://orcid.org/0000-0002-3259-8970"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dai, Zhewei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133519080","display_name":"Jiawei Zhan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhan, Jiawei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133495368","display_name":"Chengjie Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Chengjie","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5101323076"],"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":0.3149000108242035,"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":0.3149000108242035,"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/T10743","display_name":"Software Testing and Debugging Techniques","score":0.065700002014637,"subfield":{"id":"https://openalex.org/subfields/1712","display_name":"Software"},"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":0.055399999022483826,"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/foundation","display_name":"Foundation (evidence)","score":0.5364999771118164},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.48429998755455017},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.45249998569488525},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.3880999982357025},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.3686000108718872}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6934999823570251},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.5364999771118164},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.48429998755455017},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.45249998569488525},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4332999885082245},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39070001244544983},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3880999982357025},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3686000108718872},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.3472999930381775},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3440000116825104},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3409999907016754},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.33469998836517334},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.298799991607666},{"id":"https://openalex.org/C512564126","wikidata":"https://www.wikidata.org/wiki/Q7257959","display_name":"Public records","level":2,"score":0.25699999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.08915","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08915","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.08915","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08915","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":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.5547658205032349}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Existing":[0],"defect/anomaly":[1],"generation":[2,70,78,140],"methods":[3],"often":[4],"rely":[5],"on":[6,129],"few-shot":[7,138],"learning,":[8],"which":[9],"overfits":[10],"to":[11,16],"specific":[12],"defect":[13,22,33,69,77,82,93],"categories":[14],"due":[15],"the":[17],"lack":[18],"of":[19,56],"large-scale":[20,54],"paired":[21],"editing":[23,83],"data.":[24],"This":[25],"issue":[26],"is":[27],"aggravated":[28],"by":[29,50,64,116],"substantial":[30],"variations":[31],"in":[32,38,145],"scale":[34],"and":[35,43,63,79,95,100,103,124,131,141,148,151],"morphology,":[36],"resulting":[37],"limited":[39],"generalization,":[40],"degraded":[41],"realism,":[42],"category":[44],"consistency.":[45,126],"We":[46],"address":[47],"these":[48],"challenges":[49],"introducing":[51],"UDG,":[52],"a":[53,67],"dataset":[55],"300K":[57],"normal-abnormal-mask-caption":[58],"quadruplets":[59],"spanning":[60],"diverse":[61],"domains,":[62],"presenting":[65],"UniDG,":[66],"universal":[68],"foundation":[71],"model":[72],"that":[73,134],"supports":[74],"both":[75],"reference-based":[76],"text":[80],"instruction-based":[81],"without":[84],"per-category":[85],"fine-tuning.":[86],"UniDG":[87,135],"performs":[88],"Defect-Context":[89],"Editing":[90],"via":[91],"adaptive":[92],"cropping":[94],"structured":[96],"diptych":[97],"input":[98],"format,":[99],"fuses":[101],"reference":[102,125],"target":[104],"conditions":[105],"through":[106],"MM-DiT":[107],"multimodal":[108],"attention.":[109],"A":[110],"two-stage":[111],"training":[112],"strategy,":[113],"Diversity-SFT":[114],"followed":[115],"Consistency-RFT,":[117],"further":[118],"improves":[119],"diversity":[120],"while":[121],"enhancing":[122],"realism":[123],"Extensive":[127],"experiments":[128],"MVTec-AD":[130],"VisA":[132],"show":[133],"outperforms":[136],"prior":[137],"anomaly":[139,153],"image":[142],"insertion/editing":[143],"baselines":[144],"synthesis":[146],"quality":[147],"downstream":[149],"single-":[150],"multi-class":[152],"detection/localization.":[154],"Code":[155],"will":[156],"be":[157],"available":[158],"at":[159],"https://github.com/RetoFan233/UniDG.":[160]},"counts_by_year":[],"updated_date":"2026-04-14T06:08:25.285971","created_date":"2026-04-14T00:00:00"}
