{"id":"https://openalex.org/W4415537322","doi":"https://doi.org/10.1145/3746027.3754834","title":"Cluster-Aware Contrastive Multi-View Clustering Based on Masked Views","display_name":"Cluster-Aware Contrastive Multi-View Clustering Based on Masked Views","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4415537322","doi":"https://doi.org/10.1145/3746027.3754834"},"language":null,"primary_location":{"id":"doi:10.1145/3746027.3754834","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3754834","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","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/A5103196395","display_name":"Penglei Wang","orcid":"https://orcid.org/0000-0001-9469-3917"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Penglei Wang","raw_affiliation_strings":["South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9469-3917","affiliations":[{"raw_affiliation_string":"South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116142649","display_name":"Ziming Quan","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziming Quan","raw_affiliation_strings":["South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0006-9816-4290","affiliations":[{"raw_affiliation_string":"South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027472534","display_name":"Danyang Wu","orcid":"https://orcid.org/0000-0002-0309-1409"},"institutions":[{"id":"https://openalex.org/I89652312","display_name":"Northwest A&F University","ror":"https://ror.org/0051rme32","country_code":"CN","type":"education","lineage":["https://openalex.org/I89652312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Danyang Wu","raw_affiliation_strings":["Northwest A&F University, Yangling, China"],"raw_orcid":"https://orcid.org/0000-0002-0309-1409","affiliations":[{"raw_affiliation_string":"Northwest A&F University, Yangling, China","institution_ids":["https://openalex.org/I89652312"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114357209","display_name":"Jin Xu","orcid":"https://orcid.org/0009-0001-8735-3532"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin Xu","raw_affiliation_strings":["South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0001-8735-3532","affiliations":[{"raw_affiliation_string":"South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103196395"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28650316,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5942","last_page":"5950"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9969000220298767,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9969000220298767,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9948999881744385,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9908999800682068,"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/cluster-analysis","display_name":"Cluster analysis","score":0.8741999864578247},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5856000185012817},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.48010000586509705},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.4553999900817871},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.40959998965263367},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40540000796318054},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.39079999923706055},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.3797999918460846},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.3596000075340271}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8741999864578247},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6772000193595886},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5856000185012817},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5685999989509583},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.48010000586509705},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.4553999900817871},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.40959998965263367},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40540000796318054},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.39079999923706055},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38350000977516174},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.3797999918460846},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3596000075340271},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.33480000495910645},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.31540000438690186},{"id":"https://openalex.org/C177937566","wikidata":"https://www.wikidata.org/wiki/Q4223102","display_name":"Document clustering","level":3,"score":0.30889999866485596},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.2935999929904938},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29100000858306885},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2888999879360199},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.2802000045776367},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C39235581","wikidata":"https://www.wikidata.org/wiki/Q5158434","display_name":"Conceptual clustering","level":5,"score":0.27720001339912415},{"id":"https://openalex.org/C23822008","wikidata":"https://www.wikidata.org/wiki/Q5156437","display_name":"Complete-linkage clustering","level":5,"score":0.27379998564720154},{"id":"https://openalex.org/C27964816","wikidata":"https://www.wikidata.org/wiki/Q5164359","display_name":"Constrained clustering","level":5,"score":0.26260000467300415},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.2513999938964844},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746027.3754834","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3754834","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","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":22,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W2199534117","https://openalex.org/W2604723872","https://openalex.org/W2787885212","https://openalex.org/W2972477132","https://openalex.org/W2981305139","https://openalex.org/W3089039599","https://openalex.org/W3108655343","https://openalex.org/W4200226646","https://openalex.org/W4289946024","https://openalex.org/W4312973985","https://openalex.org/W4313156423","https://openalex.org/W4313648745","https://openalex.org/W4360753265","https://openalex.org/W4380875852","https://openalex.org/W4386071520","https://openalex.org/W4387968141","https://openalex.org/W4390246037","https://openalex.org/W4390873400","https://openalex.org/W4394862694","https://openalex.org/W4399146248","https://openalex.org/W4410536543"],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,34,91,149],"present":[4],"a":[5,36,60,68,109,152],"novel":[6],"Self-Supervised":[7],"Learning":[8],"(SSL)":[9],"framework":[10],"tailored":[11],"for":[12],"Multi-View":[13],"Clustering":[14],"(MVC),":[15],"which":[16],"learns":[17,41],"cross-view":[18],"semantic":[19,43],"representations":[20,44,56,135],"with":[21,71,163],"clear":[22],"clustering":[23,28,138,153,162],"boundaries":[24],"and":[25,65,101,107,140,159],"derives":[26],"balanced":[27,161],"in":[29,129],"an":[30],"end-to-end":[31],"manner.":[32],"Concretely,":[33],"propose":[35],"generative":[37],"SSL":[38,111,143],"module":[39,112],"that":[40,77],"high-level":[42],"by":[45],"recovering":[46],"randomly":[47],"masked":[48],"views":[49],"from":[50,125],"observed":[51],"views.":[52],"Then":[53],"the":[54,78,98,115,126,130,134,141,173],"extracted":[55],"are":[57],"unified":[58],"via":[59],"sample-level":[61],"local":[62],"fusion":[63],"mechanism":[64],"projected":[66],"into":[67],"unit-hypersphere":[69],"space":[70],"evenly":[72],"distributed":[73],"cluster":[74,100,121],"prototypes":[75,128],"such":[76],"pseudo":[79],"labels":[80],"can":[81],"be":[82],"directly":[83],"retrieved":[84],"using":[85],"cosine":[86],"similarity.":[87],"For":[88],"each":[89,146],"sample,":[90],"define":[92],"highly":[93],"credible":[94],"positive":[95],"pairs":[96,103],"of":[97,104,175],"same":[99],"negative":[102],"different":[105],"clusters":[106],"design":[108],"contrastive":[110],"to":[113,117,155],"force":[114],"sample":[116],"move":[118],"toward":[119],"its":[120],"prototype":[122],"while":[123],"farther":[124],"other":[127],"embedding":[131],"space.":[132],"Consequently,":[133],"exhibit":[136],"clearer":[137],"boundaries,":[139],"two":[142],"modules":[144],"benefit":[145],"other.":[147],"Finally,":[148],"further":[150],"introduce":[151],"regularizer":[154],"prevent":[156],"trivial":[157],"solutions":[158],"derive":[160],"theoretical":[164],"guarantees.":[165],"Comprehensive":[166],"evaluations":[167],"over":[168],"eight":[169],"benchmark":[170],"datasets":[171],"validate":[172],"effectiveness":[174],"our":[176],"proposals":[177],"against":[178],"ten":[179],"state-of-the-art":[180],"MVC":[181],"methods.":[182]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-25T00:00:00"}