{"id":"https://openalex.org/W4399435760","doi":"https://doi.org/10.1145/3652583.3658081","title":"Deep Image Clustering Based on Curriculum Learning and Density Information","display_name":"Deep Image Clustering Based on Curriculum Learning and Density Information","publication_year":2024,"publication_date":"2024-05-30","ids":{"openalex":"https://openalex.org/W4399435760","doi":"https://doi.org/10.1145/3652583.3658081"},"language":"en","primary_location":{"id":"doi:10.1145/3652583.3658081","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3652583.3658081","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3652583.3658081","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3652583.3658081","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088091649","display_name":"Haiyang Zheng","orcid":"https://orcid.org/0000-0001-8733-9696"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haiyang Zheng","raw_affiliation_strings":["Harbin Institute of Technology, Shenzhen, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-8733-9696","affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Shenzhen, Shenzhen, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004074785","display_name":"Ruilin Zhang","orcid":"https://orcid.org/0000-0002-4818-9282"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruilin Zhang","raw_affiliation_strings":["Harbin Institute of Technology, Shenzhen, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-4818-9282","affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Shenzhen, Shenzhen, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100608721","display_name":"Hongpeng Wang","orcid":"https://orcid.org/0000-0001-8108-2674"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongpeng Wang","raw_affiliation_strings":["Harbin Institute of Technology, Shenzhen & Peng Cheng Laboratory, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-8108-2674","affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Shenzhen & Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793","https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5088091649"],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":0.2381,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.47971191,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"330","last_page":"338"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9991000294685364,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9991000294685364,"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":0.9987000226974487,"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":0.9980000257492065,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7826047539710999},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7371039986610413},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.573134183883667},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5628054738044739},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5133108496665955},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5001494884490967},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.46188586950302124},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4213711619377136}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7826047539710999},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7371039986610413},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.573134183883667},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5628054738044739},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5133108496665955},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5001494884490967},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.46188586950302124},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4213711619377136},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3652583.3658081","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3652583.3658081","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3652583.3658081","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2604.03306","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2604.03306","pdf_url":"https://arxiv.org/pdf/2604.03306","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","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2604.03306","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03306","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"}],"best_oa_location":{"id":"doi:10.1145/3652583.3658081","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3652583.3658081","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3652583.3658081","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4399435760.pdf"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W2002427601","https://openalex.org/W2019464758","https://openalex.org/W2100659887","https://openalex.org/W2112796928","https://openalex.org/W2150102617","https://openalex.org/W2165835468","https://openalex.org/W2296073425","https://openalex.org/W2612690371","https://openalex.org/W2730106296","https://openalex.org/W2734358244","https://openalex.org/W2883725317","https://openalex.org/W2897874959","https://openalex.org/W2939176882","https://openalex.org/W2948398419","https://openalex.org/W2990604239","https://openalex.org/W3003209024","https://openalex.org/W3008997528","https://openalex.org/W3092253034","https://openalex.org/W3142849873","https://openalex.org/W3170057593","https://openalex.org/W3195170463","https://openalex.org/W3217634539","https://openalex.org/W4283800850","https://openalex.org/W4285605356","https://openalex.org/W4285606887","https://openalex.org/W4288400169","https://openalex.org/W4293731866","https://openalex.org/W4312387594","https://openalex.org/W4312459443","https://openalex.org/W4382461889"],"related_works":["https://openalex.org/W1999117613","https://openalex.org/W2040929534","https://openalex.org/W3022637481","https://openalex.org/W2393816671","https://openalex.org/W3120229345","https://openalex.org/W2111119584","https://openalex.org/W3144143113","https://openalex.org/W3039964395","https://openalex.org/W2804957450","https://openalex.org/W2357208913"],"abstract_inverted_index":{"Image":[0],"clustering":[1,17,59,95,160],"is":[2],"one":[3],"of":[4,35,48,58,129,168,180,184],"the":[5,15,33,46,54,76,84,103,126,141,146,152,166,169],"crucial":[6],"techniques":[7],"in":[8,52,80,125,178],"multimedia":[9],"analytics":[10],"and":[11,28,56,176,186],"knowledge":[12,101],"discovery.":[13],"Recently,":[14],"Deep":[16],"method":[18,96],"(DC),":[19],"characterized":[20],"by":[21],"its":[22],"ability":[23],"to":[24,71,99,150],"perform":[25],"feature":[26],"learning":[27,50,122,136],"cluster":[29,72,148,153],"assignment":[30],"jointly,":[31],"surpasses":[32],"performance":[34,57],"traditional":[36],"ones":[37],"on":[38,68,162],"image":[39,61,94,115,187],"data.":[40,62],"However,":[41],"existing":[42],"methods":[43],"rarely":[44],"consider":[45],"role":[47],"model":[49,108],"strategies":[51],"improving":[53],"robustness":[55],"complex":[60],"Furthermore,":[63],"most":[64],"approaches":[65,161],"rely":[66],"solely":[67],"point-to-point":[69],"distances":[70],"centers":[73],"for":[74,102],"partitioning":[75],"latent":[77],"representations,":[78],"resulting":[79],"error":[81],"accumulation":[82],"throughout":[83],"iterative":[85],"process.":[86],"In":[87],"this":[88],"paper,":[89],"we":[90,118,139],"propose":[91],"a":[92,107,120,133],"robust":[93],"(IDCL)":[97],"which,":[98],"our":[100],"first":[104],"time,":[105],"introduces":[106],"training":[109],"strategy":[110],"using":[111],"density":[112,127,142],"information":[113,128],"into":[114],"clustering.":[116],"Specifically,":[117],"design":[119],"curriculum":[121],"scheme":[123],"grounded":[124],"input":[130],"data,":[131],"with":[132,158],"more":[134],"reasonable":[135],"pace.":[137],"Moreover,":[138],"employ":[140],"core":[143],"rather":[144],"than":[145],"individual":[147],"center":[149],"guide":[151],"assignment.":[154],"Finally,":[155],"extensive":[156],"comparisons":[157],"state-of-the-art":[159],"benchmark":[163],"datasets":[164],"demonstrate":[165],"superiority":[167],"proposed":[170],"method,":[171],"including":[172],"robustness,":[173],"rapid":[174],"convergence,":[175],"flexibility":[177],"terms":[179],"data":[181],"scale,":[182],"number":[183],"clusters,":[185],"context.":[188]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}