{"id":"https://openalex.org/W4308233962","doi":"https://doi.org/10.1109/icip46576.2022.9897487","title":"Improving Model Adaptation for Semantic Segmentation by Learning Model-Invariant Features with Multiple Source-Domain Models","display_name":"Improving Model Adaptation for Semantic Segmentation by Learning Model-Invariant Features with Multiple Source-Domain Models","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4308233962","doi":"https://doi.org/10.1109/icip46576.2022.9897487"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9897487","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897487","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"article","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/A5016271188","display_name":"Zongyao Li","orcid":"https://orcid.org/0000-0002-3300-1806"},"institutions":[{"id":"https://openalex.org/I205349734","display_name":"Hokkaido University","ror":"https://ror.org/02e16g702","country_code":"JP","type":"education","lineage":["https://openalex.org/I205349734"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Zongyao Li","raw_affiliation_strings":["Hokkaido University,Graduate School of Information Science and Technology","Graduate School of Information Science and Technology, Hokkaido University"],"affiliations":[{"raw_affiliation_string":"Hokkaido University,Graduate School of Information Science and Technology","institution_ids":["https://openalex.org/I205349734"]},{"raw_affiliation_string":"Graduate School of Information Science and Technology, Hokkaido University","institution_ids":["https://openalex.org/I205349734"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002757875","display_name":"Ren Togo","orcid":"https://orcid.org/0000-0002-4474-3995"},"institutions":[{"id":"https://openalex.org/I205349734","display_name":"Hokkaido University","ror":"https://ror.org/02e16g702","country_code":"JP","type":"education","lineage":["https://openalex.org/I205349734"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ren Togo","raw_affiliation_strings":["Hokkaido University,Faculty of Information Science and Technology","Faculty of Information Science and Technology, Hokkaido University"],"affiliations":[{"raw_affiliation_string":"Hokkaido University,Faculty of Information Science and Technology","institution_ids":["https://openalex.org/I205349734"]},{"raw_affiliation_string":"Faculty of Information Science and Technology, Hokkaido University","institution_ids":["https://openalex.org/I205349734"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009032240","display_name":"Takahiro Ogawa","orcid":"https://orcid.org/0000-0001-5332-8112"},"institutions":[{"id":"https://openalex.org/I205349734","display_name":"Hokkaido University","ror":"https://ror.org/02e16g702","country_code":"JP","type":"education","lineage":["https://openalex.org/I205349734"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takahiro Ogawa","raw_affiliation_strings":["Hokkaido University,Faculty of Information Science and Technology","Faculty of Information Science and Technology, Hokkaido University"],"affiliations":[{"raw_affiliation_string":"Hokkaido University,Faculty of Information Science and Technology","institution_ids":["https://openalex.org/I205349734"]},{"raw_affiliation_string":"Faculty of Information Science and Technology, Hokkaido University","institution_ids":["https://openalex.org/I205349734"]}]},{"author_position":"last","author":{"id":null,"display_name":"Miki Haseyama","orcid":null},"institutions":[{"id":"https://openalex.org/I205349734","display_name":"Hokkaido University","ror":"https://ror.org/02e16g702","country_code":"JP","type":"education","lineage":["https://openalex.org/I205349734"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Miki Haseyama","raw_affiliation_strings":["Hokkaido University,Faculty of Information Science and Technology","Faculty of Information Science and Technology, Hokkaido University"],"affiliations":[{"raw_affiliation_string":"Hokkaido University,Faculty of Information Science and Technology","institution_ids":["https://openalex.org/I205349734"]},{"raw_affiliation_string":"Faculty of Information Science and Technology, Hokkaido University","institution_ids":["https://openalex.org/I205349734"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5016271188"],"corresponding_institution_ids":["https://openalex.org/I205349734"],"apc_list":null,"apc_paid":null,"fwci":0.1047,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.30900514,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"31","issue":null,"first_page":"421","last_page":"425"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9997000098228455,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9997000098228455,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9905999898910522,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9527000188827515,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8134167790412903},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6719422340393066},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5598607063293457},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5568183064460754},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5417393445968628},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.5417097806930542},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5089511871337891},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.47987547516822815},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.4655146598815918},{"id":"https://openalex.org/keywords/multi-source","display_name":"Multi-source","score":0.43213266134262085},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.41982704401016235},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4195343852043152},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.08844837546348572},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06345418095588684}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8134167790412903},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6719422340393066},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5598607063293457},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5568183064460754},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5417393445968628},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.5417097806930542},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5089511871337891},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.47987547516822815},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.4655146598815918},{"id":"https://openalex.org/C2780665216","wikidata":"https://www.wikidata.org/wiki/Q6934498","display_name":"Multi-source","level":2,"score":0.43213266134262085},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.41982704401016235},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4195343852043152},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.08844837546348572},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06345418095588684},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9897487","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897487","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2340897893","https://openalex.org/W2412782625","https://openalex.org/W2431874326","https://openalex.org/W2487365028","https://openalex.org/W2895281799","https://openalex.org/W2897093520","https://openalex.org/W2963107255","https://openalex.org/W2972285644","https://openalex.org/W2981512393","https://openalex.org/W2985406498","https://openalex.org/W3034218934","https://openalex.org/W3093803318","https://openalex.org/W3119539601","https://openalex.org/W3119635706","https://openalex.org/W3164101295","https://openalex.org/W3172292202","https://openalex.org/W3173251459","https://openalex.org/W3174142717","https://openalex.org/W3175042385","https://openalex.org/W3203098462","https://openalex.org/W4212774754","https://openalex.org/W6639480849","https://openalex.org/W6687400500","https://openalex.org/W6746282794","https://openalex.org/W6754770232","https://openalex.org/W6755271026","https://openalex.org/W6767127972","https://openalex.org/W6784532634"],"related_works":["https://openalex.org/W3201126466","https://openalex.org/W4282827391","https://openalex.org/W3095487414","https://openalex.org/W2901026139","https://openalex.org/W3171384686","https://openalex.org/W4287757915","https://openalex.org/W2576964996","https://openalex.org/W4394775207","https://openalex.org/W2958215916","https://openalex.org/W4361185727"],"abstract_inverted_index":{"In":[0,66],"this":[1,67],"paper,":[2],"we":[3,69],"focus":[4],"on":[5],"a":[6],"problem":[7,55],"remaining":[8],"to":[9,37,87,108,147],"be":[10],"studied:":[11],"multi-source":[12,19,58],"model":[13],"adaptation,":[14],"which":[15,76,106],"is":[16,61,77],"derived":[17],"from":[18,115,133],"unsupervised":[20,83],"domain":[21,59,84],"adaptation":[22,60,85,124,155],"and":[23,140,160],"replaces":[24],"the":[25,54,71,81,88,98,116,128,134,137,148,158],"source-domain":[26,29,45,100,138],"data":[27,41],"with":[28,112,127],"pre-trained":[30,118],"models.":[31],"Pre-trained":[32],"models":[33,46,101,117,125,139],"are":[34,47],"always":[35],"easier":[36],"share":[38],"than":[39],"training":[40],"so":[42],"that":[43],"multiple":[44,99],"available":[48],"in":[49,63,80,119,153],"many":[50],"practical":[51,62],"scenarios.":[52],"Therefore,":[53],"setting":[56],"of":[57,73,97,136,162],"real-world":[64],"applications.":[65],"setting,":[68],"challenge":[70],"task":[72],"semantic":[74],"segmentation":[75],"difficult":[78],"also":[79],"traditional":[82],"due":[86],"pixel-level":[89],"knowledge":[90],"transfer.":[91],"Our":[92],"method":[93],"takes":[94],"full":[95],"advantage":[96],"by":[102],"learning":[103,131],"model-invariant":[104,129],"features,":[105],"aims":[107],"obtain":[109],"target-domain":[110],"features":[111,146],"similar":[113],"distributions":[114],"different":[120],"source":[121],"domains.":[122],"The":[123],"trained":[126],"feature":[130],"benefit":[132],"diversity":[135],"can":[141],"thus":[142],"produce":[143],"more":[144],"generalizable":[145],"target":[149],"domain.":[150],"Experimental":[151],"results":[152],"several":[154],"settings":[156],"validate":[157],"effectiveness":[159],"superiority":[161],"our":[163],"method.":[164]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2025-10-10T00:00:00"}
