{"id":"https://openalex.org/W7154248257","doi":"https://doi.org/10.48550/arxiv.2604.09701","title":"PASTA: Vision Transformer Patch Aggregation for Weakly Supervised Target and Anomaly Segmentation","display_name":"PASTA: Vision Transformer Patch Aggregation for Weakly Supervised Target and Anomaly Segmentation","publication_year":2026,"publication_date":"2026-04-07","ids":{"openalex":"https://openalex.org/W7154248257","doi":"https://doi.org/10.48550/arxiv.2604.09701"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.09701","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09701","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.2604.09701","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5105251278","display_name":"Melanie Neubauer","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Neubauer, Melanie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037230603","display_name":"Elmar Rueckert","orcid":"https://orcid.org/0000-0003-1221-8253"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rueckert, Elmar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5064413203","display_name":"Christian Rauch","orcid":"https://orcid.org/0000-0003-3639-9028"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rauch, Christian","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5105251278"],"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/T10036","display_name":"Advanced Neural Network Applications","score":0.37560001015663147,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.37560001015663147,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.3206999897956848,"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":0.0340999998152256,"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/segmentation","display_name":"Segmentation","score":0.7675999999046326},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4977000057697296},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.48570001125335693},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4693000018596649},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.423799991607666},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.38350000977516174},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.37880000472068787}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7675999999046326},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7131999731063843},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.666100025177002},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4977000057697296},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.48570001125335693},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.47589999437332153},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4693000018596649},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.423799991607666},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.38350000977516174},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.37880000472068787},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3587000072002411},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.34880000352859497},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.3330000042915344},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3294000029563904},{"id":"https://openalex.org/C192097918","wikidata":"https://www.wikidata.org/wiki/Q917714","display_name":"Scrap","level":2,"score":0.3199000060558319},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2578999996185303}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.09701","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09701","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.2604.09701","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09701","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":[{"id":"https://metadata.un.org/sdg/2","score":0.7379134893417358,"display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Detecting":[0],"unseen":[1],"anomalies":[2],"in":[3,99,166],"unstructured":[4],"environments":[5],"presents":[6],"a":[7,58,87,124,131,135],"critical":[8],"challenge":[9],"for":[10,62,74],"industrial":[11,168],"and":[12,19,41,65,78,93,130,158,169],"agricultural":[13,170],"applications":[14],"such":[15],"as":[16],"material":[17],"recycling":[18,128],"weeding.":[20],"Existing":[21],"perception":[22],"systems":[23],"frequently":[24],"fail":[25],"to":[26,45,117,143,155,161],"satisfy":[27],"the":[28,112,167],"strict":[29],"operational":[30],"requirements":[31],"of":[32,76,140],"these":[33,54],"domains,":[34],"specifically":[35],"real-time":[36],"processing,":[37],"pixel-level":[38],"segmentation":[39,64,164],"precision,":[40],"robust":[42],"accuracy,":[43],"due":[44],"their":[46],"reliance":[47],"on":[48,123],"exhaustively":[49],"annotated":[50],"datasets.":[51],"To":[52],"address":[53],"limitations,":[55],"we":[56],"propose":[57],"weakly":[59],"supervised":[60],"pipeline":[61,107],"object":[63,120],"classification":[66],"using":[67],"weak":[68],"image-level":[69],"supervision":[70],"called":[71],"'Patch":[72],"Aggregation":[73],"Segmentation":[75],"Targets":[77],"Anomalies'":[79],"(PASTA).":[80],"By":[81],"comparing":[82],"an":[83],"observed":[84],"scene":[85],"with":[86],"nominal":[88],"reference,":[89],"PASTA":[90],"identifies":[91],"Target":[92,153],"Anomaly":[94,159],"objects":[95],"through":[96],"distribution":[97],"analysis":[98],"self-supervised":[100],"Vision":[101],"Transformer":[102],"(ViT)":[103],"feature":[104],"spaces.":[105],"Our":[106],"utilizes":[108],"semantic":[109],"text-prompts":[110],"via":[111],"Segment":[113],"Anything":[114],"Model":[115],"3":[116],"guide":[118],"zero-shot":[119],"segmentation.":[121],"Evaluations":[122],"custom":[125],"steel":[126],"scrap":[127],"dataset":[129,133],"plant":[132],"demonstrate":[134],"75.8%":[136],"training":[137],"time":[138],"reduction":[139],"our":[141,149],"approach":[142],"domain-specific":[144],"baselines.":[145],"While":[146],"being":[147],"domain-agnostic,":[148],"method":[150],"achieves":[151],"superior":[152],"(up":[154,160],"88.3%":[156],"IoU)":[157,163],"63.5%":[162],"performance":[165],"domain.":[171]},"counts_by_year":[],"updated_date":"2026-04-15T06:04:33.058270","created_date":"2026-04-15T00:00:00"}
