{"id":"https://openalex.org/W4413155195","doi":"https://doi.org/10.1109/cvpr52734.2025.01433","title":"CGMatch: A Different Perspective of Semi-supervised Learning","display_name":"CGMatch: A Different Perspective of Semi-supervised Learning","publication_year":2025,"publication_date":"2025-06-10","ids":{"openalex":"https://openalex.org/W4413155195","doi":"https://doi.org/10.1109/cvpr52734.2025.01433"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr52734.2025.01433","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr52734.2025.01433","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","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/A5100640937","display_name":"Bo Cheng","orcid":"https://orcid.org/0000-0002-3209-4342"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]},{"id":"https://openalex.org/I4210136497","display_name":"Jilin Medical University","ror":"https://ror.org/03mzw7781","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136497"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Cheng","raw_affiliation_strings":["Jilin University,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jilin University,China","institution_ids":["https://openalex.org/I194450716","https://openalex.org/I4210136497"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050680329","display_name":"Jueqing Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jueqing Lu","raw_affiliation_strings":["Monash University,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Monash University,Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100716461","display_name":"Yuan Tian","orcid":"https://orcid.org/0000-0003-1242-6714"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]},{"id":"https://openalex.org/I4210136497","display_name":"Jilin Medical University","ror":"https://ror.org/03mzw7781","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136497"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Tian","raw_affiliation_strings":["Jilin University,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jilin University,China","institution_ids":["https://openalex.org/I194450716","https://openalex.org/I4210136497"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089182631","display_name":"Haifeng Zhao","orcid":"https://orcid.org/0000-0002-5196-4921"},"institutions":[{"id":"https://openalex.org/I4210166603","display_name":"Jinling Institute of Technology","ror":"https://ror.org/05em1gq62","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210166603"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haifeng Zhao","raw_affiliation_strings":["Jinling Institute of Technology,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jinling Institute of Technology,China","institution_ids":["https://openalex.org/I4210166603"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029392006","display_name":"Yi Chang","orcid":"https://orcid.org/0000-0003-2697-8093"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]},{"id":"https://openalex.org/I4210136497","display_name":"Jilin Medical University","ror":"https://ror.org/03mzw7781","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136497"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Chang","raw_affiliation_strings":["Jilin University,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jilin University,China","institution_ids":["https://openalex.org/I194450716","https://openalex.org/I4210136497"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021845515","display_name":"Lan Du","orcid":"https://orcid.org/0000-0002-9925-0223"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Lan Du","raw_affiliation_strings":["Monash University,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Monash University,Australia","institution_ids":["https://openalex.org/I56590836"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.384,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.97859656,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"15381","last_page":"15391"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.4090000092983246,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.4090000092983246,"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/perspective","display_name":"Perspective (graphical)","score":0.7946515083312988},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6102001070976257},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41806477308273315},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35758525133132935}],"concepts":[{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.7946515083312988},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6102001070976257},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41806477308273315},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35758525133132935}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr52734.2025.01433","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr52734.2025.01433","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W2079057609","https://openalex.org/W2111316763","https://openalex.org/W2162531249","https://openalex.org/W2163568299","https://openalex.org/W2964020599","https://openalex.org/W2964059111","https://openalex.org/W2964159205","https://openalex.org/W2984353870","https://openalex.org/W3021294679","https://openalex.org/W3034884701","https://openalex.org/W3035160371","https://openalex.org/W3035542229","https://openalex.org/W3103649165","https://openalex.org/W3133744637","https://openalex.org/W3138507232","https://openalex.org/W3158661000","https://openalex.org/W3174231090","https://openalex.org/W3174395937","https://openalex.org/W3182493068","https://openalex.org/W3216055172","https://openalex.org/W4240805545","https://openalex.org/W4287322212","https://openalex.org/W4382631636","https://openalex.org/W4386075751","https://openalex.org/W4386076243"],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"Semi-supervised":[0],"learning":[1],"(SSL)":[2],"has":[3],"garnered":[4],"significant":[5],"attention":[6],"due":[7],"to":[8,11,23,46,76,87],"its":[9],"ability":[10],"leverage":[12],"limited":[13],"labeled":[14,56,219],"data":[15,22,220],"and":[16,40,82,176,183,201],"a":[17,109,121,145,153],"large":[18],"amount":[19],"of":[20,101,195,214],"unlabeled":[21,84,136,164],"improve":[24],"model":[25,102,112,140,199],"generalization":[26],"performance.":[27],"Recent":[28],"approaches":[29],"achieve":[30],"impressive":[31],"successes":[32],"by":[33],"combining":[34],"ideas":[35],"from":[36],"both":[37],"consistency":[38],"regularization":[39,186],"pseudo-labeling.":[41],"However,":[42],"these":[43],"methods":[44,66],"tend":[45],"underperform":[47],"in":[48,134,149],"the":[49,70,79,88,98,117,163,192,212,218],"more":[50],"realistic":[51],"situations":[52],"with":[53,143,169,187],"relatively":[54],"scarce":[55],"data.":[57],"We":[58,128],"argue":[59],"that":[60,130],"this":[61,105],"issue":[62],"arises":[63],"because":[64],"existing":[65],"rely":[67],"solely":[68],"on":[69,198,206],"model\u2019s":[71,80],"confidence,":[72,144],"making":[73],"them":[74],"challenging":[75],"accurately":[77],"assess":[78],"state":[81],"identify":[83],"examples":[85,137],"contributing":[86],"training":[89],"phase":[90],"when":[91,217],"supervision":[92],"information":[93],"is":[94,132,226],"limited,":[95],"especially":[96,216],"during":[97],"early":[99],"stages":[100],"training.":[103,141],"In":[104],"paper,":[106],"we":[107,151,190],"propose":[108,152],"novel":[110],"SSL":[111,209],"called":[113],"CGMatch,":[114],"which,":[115],"for":[116,139],"first":[118],"time,":[119],"incorporates":[120],"new":[122],"metric":[123,148],"known":[124],"as":[125],"Count-Gap":[126],"(CG).":[127],"demonstrate":[129],"CG":[131],"effective":[133],"discovering":[135],"beneficial":[138],"Along":[142],"commonly":[146],"used":[147],"SSL,":[150],"fine-grained":[154],"dynamic":[155],"selection":[156],"(FDS)":[157],"strategy.":[158],"This":[159],"strategy":[160],"dynamically":[161],"divides":[162],"dataset":[165],"into":[166],"three":[167],"subsets":[168],"different":[170],"characteristics:":[171],"easy-to-learn":[172],"set,":[173,175],"ambiguous":[174],"hard-to-learn":[177],"set.":[178],"By":[179],"selective":[180],"filtering":[181],"subsets,":[182,189],"applying":[184],"corresponding":[185],"selected":[188],"mitigate":[191],"negative":[193],"impact":[194],"incorrect":[196],"pseudo-labels":[197],"optimization":[200],"generalization.":[202],"Extensive":[203],"experimental":[204],"results":[205],"several":[207],"common":[208],"benchmarks":[210],"indicate":[211],"effectiveness":[213],"CGMatch":[215],"are":[221],"particularly":[222],"limited.":[223],"Source":[224],"code":[225],"available":[227],"at":[228],"https://github.com/BoCheng-96/CGMatch.":[229]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
