{"id":"https://openalex.org/W4394967126","doi":"https://doi.org/10.1109/lsp.2024.3391623","title":"Dual Branch Framework Using Positive and Negative Learning for Weakly Supervised Semantic Segmentation","display_name":"Dual Branch Framework Using Positive and Negative Learning for Weakly Supervised Semantic Segmentation","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4394967126","doi":"https://doi.org/10.1109/lsp.2024.3391623"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2024.3391623","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2024.3391623","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-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/A5028632899","display_name":"Yu Sang","orcid":"https://orcid.org/0009-0007-5354-9514"},"institutions":[{"id":"https://openalex.org/I176808543","display_name":"Liaoning Technical University","ror":"https://ror.org/01n2bd587","country_code":"CN","type":"education","lineage":["https://openalex.org/I176808543"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Sang","raw_affiliation_strings":["School of Electronics and Information Engineering, Liaoning Technical University, Huludao, China"],"raw_orcid":"https://orcid.org/0009-0007-5354-9514","affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Liaoning Technical University, Huludao, China","institution_ids":["https://openalex.org/I176808543"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054797814","display_name":"Tianjiao Ma","orcid":"https://orcid.org/0000-0002-2623-5874"},"institutions":[{"id":"https://openalex.org/I176808543","display_name":"Liaoning Technical University","ror":"https://ror.org/01n2bd587","country_code":"CN","type":"education","lineage":["https://openalex.org/I176808543"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianjiao Ma","raw_affiliation_strings":["School of Electronics and Information Engineering, Liaoning Technical University, Huludao, China"],"raw_orcid":"https://orcid.org/0000-0002-2623-5874","affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Liaoning Technical University, Huludao, China","institution_ids":["https://openalex.org/I176808543"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101792310","display_name":"Yunan Liu","orcid":"https://orcid.org/0000-0002-7344-8645"},"institutions":[{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunan Liu","raw_affiliation_strings":["College of Artificial Intelligence, Dalian Maritime University, Dalian, China","Yunan Liu is with the College of Artificial Intelligence, Dalian Maritime University, Dalian, China"],"raw_orcid":"https://orcid.org/0000-0002-7344-8645","affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Dalian Maritime University, Dalian, China","institution_ids":["https://openalex.org/I43313876"]},{"raw_affiliation_string":"Yunan Liu is with the College of Artificial Intelligence, Dalian Maritime University, Dalian, China","institution_ids":["https://openalex.org/I43313876"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114461975","display_name":"Tong Liu","orcid":"https://orcid.org/0000-0002-9511-8380"},"institutions":[{"id":"https://openalex.org/I176808543","display_name":"Liaoning Technical University","ror":"https://ror.org/01n2bd587","country_code":"CN","type":"education","lineage":["https://openalex.org/I176808543"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tong Liu","raw_affiliation_strings":["School of Electronics and Information Engineering, Liaoning Technical University, Huludao, China"],"raw_orcid":"https://orcid.org/0000-0002-9511-8380","affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Liaoning Technical University, Huludao, China","institution_ids":["https://openalex.org/I176808543"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100295568","display_name":"Jinguang Sun","orcid":"https://orcid.org/0000-0001-5936-6697"},"institutions":[{"id":"https://openalex.org/I176808543","display_name":"Liaoning Technical University","ror":"https://ror.org/01n2bd587","country_code":"CN","type":"education","lineage":["https://openalex.org/I176808543"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinguang Sun","raw_affiliation_strings":["School of Electronics and Information Engineering, Liaoning Technical University, Huludao, China"],"raw_orcid":"https://orcid.org/0000-0001-5936-6697","affiliations":[{"raw_affiliation_string":"School of Electronics and Information Engineering, Liaoning Technical University, Huludao, China","institution_ids":["https://openalex.org/I176808543"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6562,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.67396229,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":"31","issue":null,"first_page":"1384","last_page":"1388"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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":0.9994999766349792,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9990000128746033,"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/T12676","display_name":"Machine Learning and ELM","score":0.996399998664856,"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":0.6565238833427429},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6466406583786011},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6345393061637878},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5509202480316162},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49971461296081543},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.48904332518577576},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.38500359654426575}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6565238833427429},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6466406583786011},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6345393061637878},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5509202480316162},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49971461296081543},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.48904332518577576},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.38500359654426575},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2024.3391623","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2024.3391623","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.46000000834465027,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G3590684008","display_name":null,"funder_award_id":"61976124","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6857148376","display_name":null,"funder_award_id":"61602226","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7091968931","display_name":null,"funder_award_id":"2022M720624","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G7733246160","display_name":"\u9762\u5411\u53ef\u89c6\u5a92\u4f53\u5927\u6570\u636e\u5904\u7406\u7684\u8ba1\u7b97\u7f51\u7edc\u534f\u540c\u8c03\u5ea6\u5173\u952e\u6280\u672f\u7684\u7814\u7a76","funder_award_id":"61772112","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1923697677","https://openalex.org/W2031489346","https://openalex.org/W2108598243","https://openalex.org/W2144794286","https://openalex.org/W2161236525","https://openalex.org/W2194775991","https://openalex.org/W2295107390","https://openalex.org/W2412782625","https://openalex.org/W2558580397","https://openalex.org/W2600144439","https://openalex.org/W2798715809","https://openalex.org/W2962867364","https://openalex.org/W2981952612","https://openalex.org/W2982093251","https://openalex.org/W2991083560","https://openalex.org/W3014641072","https://openalex.org/W3034930876","https://openalex.org/W3035014997","https://openalex.org/W3035703639","https://openalex.org/W3106349512","https://openalex.org/W3122412340","https://openalex.org/W3166286626","https://openalex.org/W3173957243","https://openalex.org/W3175456851","https://openalex.org/W3176692018","https://openalex.org/W3177958285","https://openalex.org/W3183732083","https://openalex.org/W3185386766","https://openalex.org/W3196990496","https://openalex.org/W3197164375","https://openalex.org/W3202299736","https://openalex.org/W3203879378","https://openalex.org/W4200634368","https://openalex.org/W4226158211","https://openalex.org/W4226396876","https://openalex.org/W4286905173","https://openalex.org/W4294068572","https://openalex.org/W4295046718","https://openalex.org/W4313151123","https://openalex.org/W4315783894","https://openalex.org/W4377000504","https://openalex.org/W4386083104","https://openalex.org/W4389403397","https://openalex.org/W6784163774"],"related_works":["https://openalex.org/W2317351040","https://openalex.org/W4379231730","https://openalex.org/W2952466936","https://openalex.org/W4389858081","https://openalex.org/W1988622314","https://openalex.org/W2393949104","https://openalex.org/W3046201198","https://openalex.org/W4293061921","https://openalex.org/W2501551404","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Weakly":[0],"supervised":[1],"semantic":[2,69],"segmentation":[3,130,138],"(WSSS)":[4],"has":[5],"received":[6],"considerable":[7],"interest":[8],"since":[9],"it":[10],"relies":[11],"only":[12],"on":[13,163],"image-level":[14],"annotations":[15],"rather":[16],"than":[17],"fine-grained":[18],"pixel-wise":[19],"annotations,":[20],"which":[21,65,121,140],"require":[22],"vast":[23],"human":[24],"labor.":[25],"Generating":[26],"pseudo-masks":[27],"(a.k.a.":[28],"seeds)":[29],"is":[30,41],"arguably":[31],"the":[32,173],"most":[33],"standard":[34],"step":[35],"for":[36,63],"WSSS.":[37],"The":[38],"main":[39],"difficulty":[40],"that":[42,98,114,154],"seeds":[43,73,113,153],"are":[44,122,155],"usually":[45],"sparse":[46],"and":[47,60,118,125],"incomplete.":[48],"In":[49],"this":[50],"paper,":[51],"we":[52,83,105,133],"propose":[53],"a":[54,79,129,135],"dual":[55,136],"branch":[56,137],"framework":[57],"by":[58,158],"positive":[59],"negative":[61],"learning":[62],"WSSS,":[64],"distills":[66],"more":[67,123],"accurate":[68],"information":[70,100,108,146,150],"from":[71,151],"multiple":[72,94],"instead":[74],"of":[75,144],"struggling":[76],"to":[77,92,110,127],"refine":[78],"single":[80],"seed.":[81],"First,":[82],"integrate":[84],"different":[85,103],"classification":[86],"networks":[87],"with":[88],"class":[89],"activation":[90],"maps":[91],"generate":[93],"seeds.":[95,160],"Then,":[96],"considering":[97],"richer":[99],"exists":[101],"in":[102],"seeds,":[104],"perform":[106],"multi-source":[107],"distillation":[109],"obtain":[111],"aggregated":[112,159],"include":[115],"clean":[116],"labels":[117],"noisy":[119],"labels,":[120],"comprehensive":[124],"reliable":[126],"train":[128],"model.":[131],"Furthermore,":[132],"construct":[134],"network,":[139],"makes":[141],"full":[142],"use":[143],"correct":[145],"while":[147],"eliminating":[148],"incorrect":[149],"distilled":[152],"further":[156],"acquired":[157],"When":[161],"evaluated":[162],"two":[164],"benchmark":[165],"datasets,":[166],"our":[167],"method":[168],"outperforms":[169],"state-of-the-art":[170],"methods,":[171],"demonstrating":[172],"superior":[174],"performance.":[175]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
