{"id":"https://openalex.org/W2975360293","doi":"https://doi.org/10.1109/wacv45572.2020.9093626","title":"MLSL: Multi-Level Self-Supervised Learning for Domain Adaptation with Spatially Independent and Semantically Consistent Labeling","display_name":"MLSL: Multi-Level Self-Supervised Learning for Domain Adaptation with Spatially Independent and Semantically Consistent Labeling","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W2975360293","doi":"https://doi.org/10.1109/wacv45572.2020.9093626","mag":"2975360293"},"language":"en","primary_location":{"id":"doi:10.1109/wacv45572.2020.9093626","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093626","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1909.13776","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046795565","display_name":"Javed Iqbal","orcid":"https://orcid.org/0000-0003-3056-5128"},"institutions":[{"id":"https://openalex.org/I1323252656","display_name":"Information Technology University","ror":"https://ror.org/00ngv8j44","country_code":"PK","type":"education","lineage":["https://openalex.org/I1323252656"]}],"countries":["PK"],"is_corresponding":true,"raw_author_name":"Javed Iqbal","raw_affiliation_strings":["Information Technology University, Pakistan","Information Technology University,Pakistan"],"affiliations":[{"raw_affiliation_string":"Information Technology University, Pakistan","institution_ids":["https://openalex.org/I1323252656"]},{"raw_affiliation_string":"Information Technology University,Pakistan","institution_ids":["https://openalex.org/I1323252656"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101953829","display_name":"Mohsen Ali","orcid":"https://orcid.org/0000-0003-4809-8679"},"institutions":[{"id":"https://openalex.org/I1323252656","display_name":"Information Technology University","ror":"https://ror.org/00ngv8j44","country_code":"PK","type":"education","lineage":["https://openalex.org/I1323252656"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Mohsen Ali","raw_affiliation_strings":["Information Technology University, Pakistan","[Information Technology University]"],"affiliations":[{"raw_affiliation_string":"Information Technology University, Pakistan","institution_ids":["https://openalex.org/I1323252656"]},{"raw_affiliation_string":"[Information Technology University]","institution_ids":["https://openalex.org/I1323252656"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5046795565"],"corresponding_institution_ids":["https://openalex.org/I1323252656"],"apc_list":null,"apc_paid":null,"fwci":0.4114,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.67857351,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1853","last_page":"1862"},"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":0.9998999834060669,"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":0.9998999834060669,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9980000257492065,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9944000244140625,"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":0.7320988774299622},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6966748237609863},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6365163922309875},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5585101842880249},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5467655658721924},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.5165149569511414},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5145754218101501},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5107364654541016},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5036322474479675},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.503487765789032},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.48143547773361206},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4498044550418854},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40153196454048157},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14924201369285583},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09083843231201172}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7320988774299622},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6966748237609863},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6365163922309875},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5585101842880249},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5467655658721924},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.5165149569511414},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5145754218101501},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5107364654541016},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5036322474479675},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.503487765789032},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.48143547773361206},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4498044550418854},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40153196454048157},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14924201369285583},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09083843231201172},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/wacv45572.2020.9093626","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093626","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1909.13776","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1909.13776","pdf_url":"https://arxiv.org/pdf/1909.13776","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-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.1909.13776","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1909.13776","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"},{"id":"mag:2975360293","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1909.13776","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1909.13776","pdf_url":"https://arxiv.org/pdf/1909.13776","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-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8399999737739563,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2975360293.pdf","grobid_xml":"https://content.openalex.org/works/W2975360293.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W1745334888","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2095389390","https://openalex.org/W2099471712","https://openalex.org/W2117539524","https://openalex.org/W2150066425","https://openalex.org/W2186615578","https://openalex.org/W2340897893","https://openalex.org/W2412782625","https://openalex.org/W2431874326","https://openalex.org/W2487365028","https://openalex.org/W2558580397","https://openalex.org/W2560023338","https://openalex.org/W2562192638","https://openalex.org/W2739759330","https://openalex.org/W2795889831","https://openalex.org/W2883090707","https://openalex.org/W2895281799","https://openalex.org/W2932414082","https://openalex.org/W2962808524","https://openalex.org/W2962976523","https://openalex.org/W2963073217","https://openalex.org/W2963107255","https://openalex.org/W2963120918","https://openalex.org/W2963343500","https://openalex.org/W2963481481","https://openalex.org/W2963789515","https://openalex.org/W2963864946","https://openalex.org/W2963881378","https://openalex.org/W2963983207","https://openalex.org/W2964115968","https://openalex.org/W2964288706","https://openalex.org/W2969893028","https://openalex.org/W2985409929","https://openalex.org/W6639824700","https://openalex.org/W6640295612","https://openalex.org/W6686509673","https://openalex.org/W6722836162","https://openalex.org/W6730623217","https://openalex.org/W6746282794"],"related_works":["https://openalex.org/W3009328718","https://openalex.org/W3159212271","https://openalex.org/W3205171001","https://openalex.org/W3153264591","https://openalex.org/W3110486195","https://openalex.org/W3044881747","https://openalex.org/W3014661880","https://openalex.org/W3149860822","https://openalex.org/W3157821768","https://openalex.org/W2551835155","https://openalex.org/W2896591327","https://openalex.org/W2767657961","https://openalex.org/W2963182609","https://openalex.org/W3185527523","https://openalex.org/W3206212295","https://openalex.org/W3176713420","https://openalex.org/W3177192381","https://openalex.org/W2562192638","https://openalex.org/W2963118547","https://openalex.org/W2900553892"],"abstract_inverted_index":{"Most":[0],"of":[1,58,69,79,154,182],"the":[2,62,70,133,144,155],"recent":[3],"Deep":[4],"Semantic":[5],"Segmentation":[6],"algorithms":[7],"suffer":[8],"from":[9],"large":[10,36],"generalization":[11],"errors,":[12],"even":[13,135],"when":[14,136],"powerful":[15],"hierarchical":[16],"representation":[17,134],"models,":[18],"based":[19],"on":[20,184,191],"convolutional":[21],"neural":[22],"networks,":[23],"have":[24],"been":[25],"employed.":[26],"This":[27],"could":[28],"be":[29,75],"attributed":[30],"to":[31,110,143,152,186,193,197],"limited":[32],"training":[33],"data":[34],"and":[35,41,86,98,121,175,189],"distribution":[37],"gap":[38],"in":[39,119],"train":[40],"test":[42],"domain":[43,56,112,123,145],"datasets.":[44],"In":[45],"this":[46],"paper,":[47],"we":[48,82,178],"propose":[49],"a":[50],"multi-level":[51,158],"self-supervised":[52],"learning":[53,160],"model":[54,97],"for":[55,149],"adaptation":[57,113,188,195],"semantic":[59],"segmentation.":[60],"Exploiting":[61],"idea":[63],"that":[64],"an":[65,100,180],"object":[66,148],"(and":[67],"most":[68],"stuff":[71],"given":[72],"context)":[73],"should":[74],"labeled":[76],"consistently":[77],"regardless":[78],"its":[80],"location,":[81],"generate":[83],"spatially":[84],"independent":[85],"semantically":[87],"consistent":[88],"(SISC)":[89],"pseudo-labels":[90],"by":[91,114],"segmenting":[92],"multiple":[93],"sub-images":[94],"using":[95],"base":[96],"designing":[99],"aggregation":[101],"strategy.":[102],"Image":[103],"level":[104],"pseudo":[105],"weak-labels,":[106],"PWL,":[107],"are":[108,138],"computed":[109],"guide":[111],"capturing":[115],"global":[116],"context":[117],"similarity":[118],"source":[120],"target":[122],"at":[124],"latent":[125,130],"space":[126,131],"level.":[127],"Thus":[128],"helping":[129],"learn":[132],"there":[137],"very":[139],"few":[140],"pixels":[141],"belonging":[142],"category":[146],"(small":[147],"example)":[150],"compared":[151,196],"rest":[153],"image.":[156],"Our":[157],"Self-supervised":[159],"(MLSL)":[161],"outperforms":[162],"existing":[163,198],"state-of-art":[164,199],"(self":[165],"or":[166],"adversarial":[167],"learning)":[168],"algorithms.":[169],"Specifically,":[170],"keeping":[171],"all":[172],"setting":[173],"similar":[174],"employing":[176],"MLSL":[177],"obtain":[179],"mIoUgain":[181],"5.1%":[183],"GTA-V":[185],"Cityscapes":[187,194],"4.3%":[190],"SYNTHIA":[192],"method.":[200]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
