{"id":"https://openalex.org/W4321636855","doi":"https://doi.org/10.1109/wcsp55476.2022.10039258","title":"UM-Net: An MLP-based Image Segmentation Network","display_name":"UM-Net: An MLP-based Image Segmentation Network","publication_year":2022,"publication_date":"2022-11-01","ids":{"openalex":"https://openalex.org/W4321636855","doi":"https://doi.org/10.1109/wcsp55476.2022.10039258"},"language":"en","primary_location":{"id":"doi:10.1109/wcsp55476.2022.10039258","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/wcsp55476.2022.10039258","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on Wireless Communications and Signal Processing (WCSP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100461515","display_name":"Xinwei Liu","orcid":"https://orcid.org/0000-0002-9829-3395"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinwei Liu","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications,Nanjing,China","Nanjing University of Posts and Telecommunications, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications,Nanjing,China","institution_ids":["https://openalex.org/I41198531"]},{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100744866","display_name":"Li Huang","orcid":"https://orcid.org/0009-0005-4711-3963"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Huang","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications,Nanjing,China","Nanjing University of Posts and Telecommunications, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications,Nanjing,China","institution_ids":["https://openalex.org/I41198531"]},{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071762112","display_name":"Jian Xiao","orcid":"https://orcid.org/0000-0002-0684-2896"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Xiao","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications,Nanjing,China","Nanjing University of Posts and Telecommunications, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications,Nanjing,China","institution_ids":["https://openalex.org/I41198531"]},{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I41198531"],"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":"89","last_page":"94"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9873999953269958,"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":0.9873999953269958,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9866999983787537,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9771000146865845,"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/computer-science","display_name":"Computer science","score":0.7903447151184082},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6209197640419006},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.620593786239624},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.6049061417579651},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5352469086647034},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5151659250259399},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4557255804538727},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.44600334763526917},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42436331510543823},{"id":"https://openalex.org/keywords/network-performance","display_name":"Network performance","score":0.4200161397457123},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2387526035308838},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08491969108581543}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7903447151184082},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6209197640419006},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.620593786239624},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.6049061417579651},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5352469086647034},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5151659250259399},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4557255804538727},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.44600334763526917},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42436331510543823},{"id":"https://openalex.org/C203274722","wikidata":"https://www.wikidata.org/wiki/Q7001161","display_name":"Network performance","level":2,"score":0.4200161397457123},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2387526035308838},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08491969108581543},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wcsp55476.2022.10039258","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/wcsp55476.2022.10039258","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on Wireless Communications and Signal Processing (WCSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2884436604","https://openalex.org/W2991372685","https://openalex.org/W3137963805","https://openalex.org/W3157506437","https://openalex.org/W3181848549","https://openalex.org/W4295934721","https://openalex.org/W4312853765"],"related_works":["https://openalex.org/W2062399876","https://openalex.org/W2607795551","https://openalex.org/W3155117723","https://openalex.org/W1991429770","https://openalex.org/W1983892167","https://openalex.org/W2281134365","https://openalex.org/W4310746709","https://openalex.org/W4306309518","https://openalex.org/W4385574037","https://openalex.org/W4386075645"],"abstract_inverted_index":{"In":[0],"the":[1,17,32,35,62,72,77,82,95,106,109,113,118,149,153,163,168,176,189,193,221,232,238,241],"field":[2],"of":[3,16,23,37,71,97,108,152,234,237],"image":[4,128],"segmentation":[5,29,129,208],"based":[6,139],"on":[7,105,140,188],"deep":[8,78,150,169],"learning,":[9],"U-shaped":[10,64],"network":[11,20,65,79,130,138,154,177,239],"has":[12,53],"always":[13],"been":[14],"one":[15],"most":[18],"popular":[19],"frameworks":[21],"because":[22],"its":[24],"unique":[25],"structure":[26,36,107,151],"and":[27,41,45,52,81,93,111,142,160,213,217],"wonderful":[28],"effect.":[30],"At":[31,220],"same":[33,222],"time,":[34,223],"MLP":[38,84],"is":[39,133,136],"simple":[40],"compared":[42,198],"with":[43,199,210],"CNN":[44],"transformer,":[46],"it":[47,157,181],"introduces":[48],"less":[49],"inductive":[50],"bias":[51],"stronger":[54],"generalization":[55],"performance.":[56,242],"This":[57],"paper":[58],"aims":[59],"at":[60],"that":[61,197],"current":[63,201],"does":[66],"not":[67],"make":[68,156,180],"full":[69],"use":[70],"rich":[73,164],"local":[74,165],"information":[75,96],"in":[76,100,120,167],"structure,":[80,170],"existing":[83],"model":[85],"directly":[86],"uses":[87],"fixed":[88],"weights":[89],"for":[90],"feature":[91],"aggregation":[92],"ignores":[94],"different":[98,101,227],"semantics":[99],"images,":[102],"focusing":[103],"only":[104],"encoder":[110],"ignoring":[112],"performance":[114],"improvement":[115],"brought":[116],"by":[117],"decoder,":[119],"order":[121],"to":[122,148,155,175,179,230,240],"solve":[123],"above":[124],"problems,":[125],"a":[126,137],"new":[127],"named":[131],"UM-Net":[132,187,205],"proposed.":[134],"It":[135],"convolution":[141],"MLP,":[143],"we":[144,224],"add":[145],"Wave-MLP":[146],"blocks":[147],"fully":[158],"utilize":[159],"dynamically":[161],"aggregate":[162],"features":[166],"using":[171],"decoupled":[172],"dynamic":[173],"filters":[174],"upsampling":[178],"content":[182],"adaptive.":[183],"We":[184],"test":[185],"our":[186],"standard":[190],"BUSI":[191],"dataset,":[192],"experimental":[194],"results":[195],"show":[196],"several":[200],"mainstream":[202],"baseline":[203],"networks,":[204],"achieves":[206],"better":[207],"performance,":[209],"F1":[211],"score":[212],"IoU":[214],"reaching":[215],"80.77%":[216],"68.66%":[218],"respectively.":[219],"also":[225],"conducted":[226],"ablation":[228],"experiments":[229],"explore":[231],"contribution":[233],"each":[235],"module":[236]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}