{"id":"https://openalex.org/W7131374947","doi":"https://doi.org/10.48550/arxiv.2602.19206","title":"GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learning","display_name":"GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learning","publication_year":2026,"publication_date":"2026-02-22","ids":{"openalex":"https://openalex.org/W7131374947","doi":"https://doi.org/10.48550/arxiv.2602.19206"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.19206","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.19206","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2602.19206","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102586471","display_name":"Zehao Deng","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Deng, Zehao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126838418","display_name":"An Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, An","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126783650","display_name":"Yan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5102586471"],"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":0.8939999938011169,"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":0.8939999938011169,"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":0.038100000470876694,"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":0.028699999675154686,"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":0.5932999849319458},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5794000029563904},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5476999878883362},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.47290000319480896},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4239000082015991},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.3968000113964081},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.3840000033378601},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.3711000084877014},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3395000100135803}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7423999905586243},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6504999995231628},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5932999849319458},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5794000029563904},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5476999878883362},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.47290000319480896},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4239000082015991},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.3968000113964081},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3889000117778778},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3840000033378601},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36309999227523804},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3395000100135803},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.3222000002861023},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2849999964237213},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.27799999713897705},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.27149999141693115},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.26330000162124634},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.25769999623298645},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2547999918460846}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.19206","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.19206","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.19206","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.19206","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":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Zero-shot":[0],"3D":[1,43,119],"Anomaly":[2],"Detection":[3],"is":[4,23],"an":[5,68],"emerging":[6],"task":[7],"that":[8,150,184],"aims":[9],"to":[10,75,100],"detect":[11,76],"anomalies":[12,103],"in":[13,26,156,189],"a":[14,63,105],"target":[15,19],"dataset":[16],"without":[17],"any":[18],"training":[20],"data,":[21],"which":[22,96],"particularly":[24],"important":[25],"scenarios":[27],"constrained":[28],"by":[29,41,133],"sample":[30],"scarcity":[31],"and":[32,59,89,128,153],"data":[33],"privacy":[34],"concerns.":[35],"While":[36],"current":[37],"methods":[38],"adapt":[39],"CLIP":[40],"projecting":[42],"point":[44],"clouds":[45],"into":[46],"2D":[47,65],"representations,":[48],"they":[49],"face":[50],"challenges.":[51],"The":[52],"projection":[53],"inherently":[54],"loses":[55],"some":[56],"geometric":[57,102,120],"details,":[58],"the":[60,86,98,165],"reliance":[61],"on":[62,171,178],"single":[64],"modality":[66],"provides":[67],"incomplete":[69],"visual":[70],"understanding,":[71],"limiting":[72],"their":[73,172],"ability":[74],"diverse":[77],"anomaly":[78],"types.":[79],"To":[80],"address":[81],"these":[82],"limitations,":[83],"we":[84,112,143],"propose":[85],"Geometry-Aware":[87],"Prompt":[88],"Synergistic":[90,145,159],"View":[91,146],"Representation":[92,147],"Learning":[93,148],"(GS-CLIP)":[94],"framework,":[95],"enables":[97],"model":[99],"identify":[101],"through":[104],"two-stage":[106],"learning":[107],"process.":[108],"In":[109,140],"stage":[110,141],"1,":[111],"dynamically":[113],"generate":[114],"text":[115],"prompts":[116,123],"embedded":[117],"with":[118],"priors.":[121],"These":[122],"contain":[124],"global":[125],"shape":[126],"context":[127],"local":[129],"defect":[130],"information":[131],"distilled":[132],"our":[134],"Geometric":[135],"Defect":[136],"Distillation":[137],"Module":[138,161],"(GDDM).":[139],"2,":[142],"introduce":[144],"architecture":[149],"processes":[151],"rendered":[152],"depth":[154],"images":[155],"parallel.":[157],"A":[158],"Refinement":[160],"(SRM)":[162],"subsequently":[163],"fuses":[164],"features":[166],"of":[167],"both":[168],"streams,":[169],"capitalizing":[170],"complementary":[173],"strengths.":[174],"Comprehensive":[175],"experimental":[176],"results":[177],"four":[179],"large-scale":[180],"public":[181],"datasets":[182],"show":[183],"GS-CLIP":[185],"achieves":[186],"superior":[187],"performance":[188],"detection.":[190],"Code":[191],"can":[192],"be":[193],"available":[194],"at":[195],"https://github.com/zhushengxinyue/GS-CLIP.":[196]},"counts_by_year":[],"updated_date":"2026-02-26T06:34:08.959763","created_date":"2026-02-26T00:00:00"}
