{"id":"https://openalex.org/W4392430054","doi":"https://doi.org/10.48550/arxiv.2402.19422","title":"PEM: Prototype-based Efficient MaskFormer for Image Segmentation","display_name":"PEM: Prototype-based Efficient MaskFormer for Image Segmentation","publication_year":2024,"publication_date":"2024-02-29","ids":{"openalex":"https://openalex.org/W4392430054","doi":"https://doi.org/10.48550/arxiv.2402.19422"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2402.19422","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2402.19422","pdf_url":"https://arxiv.org/pdf/2402.19422","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2402.19422","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5081060854","display_name":"Niccol\u00f2 Cavagnero","orcid":"https://orcid.org/0000-0002-6180-7786"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Cavagnero, Niccol\u00f2","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5094064138","display_name":"Gabriele Rosi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rosi, Gabriele","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093577511","display_name":"Claudia Cuttano","orcid":"https://orcid.org/0009-0004-9672-507X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cuttano, Claudia","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016675020","display_name":"Francesca Pistilli","orcid":"https://orcid.org/0000-0001-9372-032X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pistilli, Francesca","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102861496","display_name":"Marco Ciccone","orcid":"https://orcid.org/0000-0002-3306-1323"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ciccone, Marco","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050576327","display_name":"Giuseppe Averta","orcid":"https://orcid.org/0000-0003-1212-3465"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Averta, Giuseppe","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5077593505","display_name":"Fabio Cermelli","orcid":"https://orcid.org/0000-0001-7077-697X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cermelli, Fabio","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5081060854"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9215999841690063,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9215999841690063,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5662487745285034},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5488796234130859},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5454477071762085},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5119363069534302},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4912876486778259},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.42990729212760925},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3271061182022095}],"concepts":[{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5662487745285034},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5488796234130859},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5454477071762085},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5119363069534302},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4912876486778259},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.42990729212760925},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3271061182022095}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2402.19422","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2402.19422","pdf_url":"https://arxiv.org/pdf/2402.19422","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2402.19422","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2402.19422","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":"pmh:oai:arXiv.org:2402.19422","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2402.19422","pdf_url":"https://arxiv.org/pdf/2402.19422","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4508289328","display_name":null,"funder_award_id":"PE00000013","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G7248538987","display_name":null,"funder_award_id":"1555 11/10/2022","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G8893660128","display_name":null,"funder_award_id":"PE0000001","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4392430054.pdf","grobid_xml":"https://content.openalex.org/works/W4392430054.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4379231730","https://openalex.org/W4389858081","https://openalex.org/W4324315429","https://openalex.org/W2501551404","https://openalex.org/W4385583601","https://openalex.org/W4298131179","https://openalex.org/W2113201962","https://openalex.org/W2799953226","https://openalex.org/W4366343436","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Recent":[0],"transformer-based":[1,71],"architectures":[2,41,170],"have":[3,120],"shown":[4],"impressive":[5,38],"results":[6],"in":[7,21,76,124],"the":[8,88,95,99,103,130,140],"field":[9],"of":[10,90,116,132],"image":[11],"segmentation.":[12],"Thanks":[13],"to":[14,93,129],"their":[15],"flexibility,":[16],"they":[17],"obtain":[18],"outstanding":[19,161],"performance":[20,162],"multiple":[22,77],"segmentation":[23,78],"tasks,":[24,146],"such":[25,37],"as":[26],"semantic":[27,122,147],"and":[28,45,97,135,148,157,166,174],"panoptic,":[29],"under":[30],"a":[31,82],"single":[32],"unified":[33],"framework.":[34],"To":[35,59],"achieve":[36],"performance,":[39],"these":[40],"employ":[42],"intensive":[43],"operations":[44],"require":[46],"substantial":[47],"computational":[48],"resources,":[49],"which":[50,86],"are":[51],"often":[52],"not":[53],"available,":[54],"especially":[55],"on":[56,144,152,163],"edge":[57],"devices.":[58],"fill":[60],"this":[61],"gap,":[62],"we":[63],"propose":[64],"Prototype-based":[65],"Efficient":[66],"MaskFormer":[67],"(PEM),":[68],"an":[69,109,125],"efficient":[70,110,126],"architecture":[72,143],"that":[73,119],"can":[74],"operate":[75],"tasks.":[79],"PEM":[80,107,142,159],"proposes":[81],"novel":[83],"prototype-based":[84],"cross-attention":[85],"leverages":[87],"redundancy":[89],"visual":[91],"features":[92,118],"restrict":[94],"computation":[96],"improve":[98],"efficiency":[100],"without":[101],"harming":[102],"performance.":[104],"In":[105],"addition,":[106],"introduces":[108],"multi-scale":[111],"feature":[112],"pyramid":[113],"network,":[114],"capable":[115],"extracting":[117],"high":[121],"content":[123],"way,":[127],"thanks":[128],"combination":[131],"deformable":[133],"convolutions":[134],"context-based":[136],"self-modulation.":[137],"We":[138],"benchmark":[139],"proposed":[141],"two":[145,153],"panoptic":[149],"segmentation,":[150],"evaluated":[151],"different":[154],"datasets,":[155],"Cityscapes":[156],"ADE20K.":[158],"demonstrates":[160],"every":[164],"task":[165],"dataset,":[167],"outperforming":[168],"task-specific":[169],"while":[171],"being":[172],"comparable":[173],"even":[175],"better":[176],"than":[177],"computationally-expensive":[178],"baselines.":[179]},"counts_by_year":[],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}