{"id":"https://openalex.org/W4406612399","doi":"https://doi.org/10.1109/smc54092.2024.10831685","title":"Top-K Pooling with Patch Contrastive Learning for Weakly-Supervised Semantic Segmentation","display_name":"Top-K Pooling with Patch Contrastive Learning for Weakly-Supervised Semantic Segmentation","publication_year":2024,"publication_date":"2024-10-06","ids":{"openalex":"https://openalex.org/W4406612399","doi":"https://doi.org/10.1109/smc54092.2024.10831685"},"language":"en","primary_location":{"id":"doi:10.1109/smc54092.2024.10831685","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc54092.2024.10831685","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5102088294","display_name":"Wangyu Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wangyu Wu","raw_affiliation_strings":["Xi&#x0027;an Jiaotong-Liverpool University"],"affiliations":[{"raw_affiliation_string":"Xi&#x0027;an Jiaotong-Liverpool University","institution_ids":["https://openalex.org/I69356397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043844098","display_name":"Tianhong Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I195460627","display_name":"University of Aberdeen","ror":"https://ror.org/016476m91","country_code":"GB","type":"education","lineage":["https://openalex.org/I195460627"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Tianhong Dai","raw_affiliation_strings":["University of Aberdeen"],"affiliations":[{"raw_affiliation_string":"University of Aberdeen","institution_ids":["https://openalex.org/I195460627"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049705331","display_name":"Xiaowei Huang","orcid":"https://orcid.org/0009-0006-1617-3071"},"institutions":[{"id":"https://openalex.org/I146655781","display_name":"University of Liverpool","ror":"https://ror.org/04xs57h96","country_code":"GB","type":"education","lineage":["https://openalex.org/I146655781"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Xiaowei Huang","raw_affiliation_strings":["The University of Liverpool"],"affiliations":[{"raw_affiliation_string":"The University of Liverpool","institution_ids":["https://openalex.org/I146655781"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113773882","display_name":"Fei Ma","orcid":"https://orcid.org/0000-0003-2563-3296"},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Ma","raw_affiliation_strings":["Xi&#x0027;an Jiaotong-Liverpool University"],"affiliations":[{"raw_affiliation_string":"Xi&#x0027;an Jiaotong-Liverpool University","institution_ids":["https://openalex.org/I69356397"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011918180","display_name":"Jimin Xiao","orcid":"https://orcid.org/0000-0002-9416-2486"},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jimin Xiao","raw_affiliation_strings":["Xi&#x0027;an Jiaotong-Liverpool University"],"affiliations":[{"raw_affiliation_string":"Xi&#x0027;an Jiaotong-Liverpool University","institution_ids":["https://openalex.org/I69356397"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5102088294"],"corresponding_institution_ids":["https://openalex.org/I69356397"],"apc_list":null,"apc_paid":null,"fwci":3.149,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.93258609,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5270","last_page":"5275"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9175999760627747,"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":0.9175999760627747,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9047999978065491,"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/pooling","display_name":"Pooling","score":0.7733760476112366},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7576959729194641},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6785404086112976},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6413962244987488},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5902963280677795},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3800360858440399},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33081355690956116}],"concepts":[{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.7733760476112366},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7576959729194641},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6785404086112976},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6413962244987488},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5902963280677795},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3800360858440399},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33081355690956116}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc54092.2024.10831685","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc54092.2024.10831685","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1794522962","display_name":null,"funder_award_id":"61972323","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8012633139","display_name":null,"funder_award_id":"2022YFE0200300","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program 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/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W2031489346","https://openalex.org/W2144794286","https://openalex.org/W2161236525","https://openalex.org/W2295107390","https://openalex.org/W2306289963","https://openalex.org/W2412782625","https://openalex.org/W2600144439","https://openalex.org/W2787091153","https://openalex.org/W2962758679","https://openalex.org/W2962867364","https://openalex.org/W2982093251","https://openalex.org/W3034373787","https://openalex.org/W3035703639","https://openalex.org/W3107653507","https://openalex.org/W3112010173","https://openalex.org/W3160854032","https://openalex.org/W3177958285","https://openalex.org/W4286905173","https://openalex.org/W4288438155","https://openalex.org/W4312509967","https://openalex.org/W4312566218","https://openalex.org/W4312680544","https://openalex.org/W4312836939","https://openalex.org/W4313153210","https://openalex.org/W4372337945","https://openalex.org/W4384408620","https://openalex.org/W4386066092","https://openalex.org/W4390191228","https://openalex.org/W4390872682","https://openalex.org/W4392903172","https://openalex.org/W4392904609","https://openalex.org/W4392910311","https://openalex.org/W4393442792","https://openalex.org/W4402101108","https://openalex.org/W4402351971"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W147410782","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W3022252430","https://openalex.org/W4287804464","https://openalex.org/W3103989898","https://openalex.org/W3211292372","https://openalex.org/W803346624"],"abstract_inverted_index":{"Weakly":[0],"Supervised":[1],"Semantic":[2],"Segmentation":[3],"(WSSS)":[4],"using":[5,39],"only":[6],"image-level":[7,65],"labels":[8,35,74],"has":[9],"gained":[10],"significant":[11],"attention":[12],"due":[13,75],"to":[14,49,58,63,76,107,124,129],"cost-effectiveness.":[15],"Recently,":[16],"Vision":[17],"Transformer":[18],"(ViT)":[19],"based":[20],"methods":[21,38,45,150],"without":[22],"class":[23],"activation":[24],"map":[25,59],"(CAM)":[26],"have":[27],"shown":[28],"greater":[29],"capability":[30],"in":[31],"generating":[32],"reliable":[33],"pseudo":[34,73],"than":[36],"previous":[37,112],"CAM.":[40],"However,":[41],"the":[42,51,54,60,64,70,77,81,109,126,132,152],"current":[43],"ViT-based":[44,90],"utilize":[46],"max":[47,113],"pooling":[48,95,105,114],"select":[50],"patch":[52,97,117,127],"with":[53,96],"highest":[55],"prediction":[56],"score":[57],"patch-level":[61],"classification":[62,79],"one,":[66],"which":[67,101],"may":[68],"affect":[69],"quality":[71],"of":[72,80,111],"inaccurate":[78],"patches.":[82],"In":[83],"this":[84],"paper,":[85],"we":[86],"introduce":[87],"a":[88,103],"novel":[89],"WSSS":[91,149],"method":[92],"named":[93],"top-K":[94,104],"contrastive":[98,118],"learning":[99],"(TKP-PCL),":[100],"employs":[102],"layer":[106],"alleviate":[108],"limitations":[110],"selection.":[115],"A":[116],"error":[119],"(PCE)":[120],"is":[121,142],"also":[122],"proposed":[123],"enhance":[125],"embeddings":[128],"further":[130],"improve":[131],"final":[133],"results.":[134],"The":[135],"experimental":[136],"results":[137],"show":[138],"that":[139],"our":[140],"approach":[141],"very":[143],"efficient":[144],"and":[145,156],"outperforms":[146],"other":[147],"state-of-the-art":[148],"on":[151],"PASCAL":[153],"VOC":[154],"2012":[155],"MS":[157],"COCO":[158],"2014":[159],"dataset.":[160]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
