{"id":"https://openalex.org/W7153126180","doi":"https://doi.org/10.48550/arxiv.2604.08301","title":"GroundingAnomaly: Spatially-Grounded Diffusion for Few-Shot Anomaly Synthesis","display_name":"GroundingAnomaly: Spatially-Grounded Diffusion for Few-Shot Anomaly Synthesis","publication_year":2026,"publication_date":"2026-04-09","ids":{"openalex":"https://openalex.org/W7153126180","doi":"https://doi.org/10.48550/arxiv.2604.08301"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.08301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08301","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.2604.08301","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034806412","display_name":"Yishen Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yishen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133330903","display_name":"Hongcang Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Hongcang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133317089","display_name":"Pengcheng Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Pengcheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102547463","display_name":"Yunfan Bao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bao, Yunfan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133391966","display_name":"Yuxi Tian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian, Yuxi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133338473","display_name":"Jieming Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jieming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133337174","display_name":"Hao Chen","orcid":"https://orcid.org/0009-0007-5549-0421"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013934818","display_name":"Zheng Zhi","orcid":"https://orcid.org/0000-0001-5964-7958"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhi, Zheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133321164","display_name":"Yongchun Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yongchun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133380979","display_name":"Ying Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Ying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133344438","display_name":"Dongpu Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Dongpu","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":0.7207000255584717,"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":0.7207000255584717,"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":0.0786999985575676,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.057100001722574234,"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":0.692300021648407},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.6323999762535095},{"id":"https://openalex.org/keywords/downstream","display_name":"Downstream (manufacturing)","score":0.40619999170303345},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4032000005245209},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.39660000801086426},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38530001044273376},{"id":"https://openalex.org/keywords/inpainting","display_name":"Inpainting","score":0.38040000200271606}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.692300021648407},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.6323999762535095},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6128000020980835},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48739999532699585},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.40619999170303345},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4032000005245209},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.39660000801086426},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38530001044273376},{"id":"https://openalex.org/C11727466","wikidata":"https://www.wikidata.org/wiki/Q1628157","display_name":"Inpainting","level":3,"score":0.38040000200271606},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36649999022483826},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34139999747276306},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.3400000035762787},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3151000142097473},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.2888999879360199},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.25940001010894775}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.08301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08301","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.2604.08301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08301","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"performance":[1,134],"of":[2,16],"visual":[3],"anomaly":[4,21,62,140],"inspection":[5],"in":[6],"industrial":[7],"quality":[8],"control":[9,82],"is":[10,92],"often":[11],"constrained":[12],"by":[13,44],"the":[14,84,119],"scarcity":[15],"real":[17],"anomalous":[18],"samples.":[19],"Consequently,":[20],"synthesis":[22],"techniques":[23],"have":[24],"been":[25],"developed":[26],"to":[27,48,78,94],"enlarge":[28],"training":[29],"sets":[30],"and":[31,122,131,143],"enhance":[32],"downstream":[33,137],"inspection.":[34],"However,":[35],"existing":[36],"methods":[37],"either":[38],"suffer":[39],"from":[40],"poor":[41],"integration":[42],"caused":[43],"inpainting":[45],"or":[46],"fail":[47],"provide":[49],"accurate":[50],"masks.":[51],"To":[52],"address":[53],"these":[54],"limitations,":[55],"we":[56],"propose":[57],"GroundingAnomaly,":[58],"a":[59,69,88,99],"novel":[60],"few-shot":[61,114],"image":[63],"generation":[64],"framework.":[65],"Our":[66],"framework":[67],"introduces":[68],"Spatial":[70],"Conditioning":[71],"Module":[72,91],"that":[73,126],"leverages":[74],"per-pixel":[75],"semantic":[76],"maps":[77],"enable":[79],"precise":[80],"spatial":[81],"over":[83],"synthesized":[85],"anomalies.":[86],"Furthermore,":[87],"Gated":[89],"Self-Attention":[90],"designed":[93],"inject":[95],"conditioning":[96],"tokens":[97],"into":[98],"frozen":[100],"U-Net":[101],"via":[102],"gated":[103],"attention":[104],"layers.":[105],"This":[106],"carefully":[107],"preserves":[108],"pretrained":[109],"priors":[110],"while":[111],"ensuring":[112],"stable":[113],"adaptation.":[115],"Extensive":[116],"evaluations":[117],"on":[118],"MVTec":[120],"AD":[121],"VisA":[123],"datasets":[124],"demonstrate":[125],"GroundingAnomaly":[127],"generates":[128],"high-quality":[129],"anomalies":[130],"achieves":[132],"state-of-the-art":[133],"across":[135],"multiple":[136],"tasks,":[138],"including":[139],"detection,":[141],"segmentation,":[142],"instance-level":[144],"detection.":[145]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-11T00:00:00"}