{"id":"https://openalex.org/W7154158099","doi":"https://doi.org/10.1145/3772318.3791278","title":"Contrastive Learning for Large-scale Color-Name Dataset: Tackling Sparsity with Negative Sampling","display_name":"Contrastive Learning for Large-scale Color-Name Dataset: Tackling Sparsity with Negative Sampling","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154158099","doi":"https://doi.org/10.1145/3772318.3791278"},"language":null,"primary_location":{"id":"doi:10.1145/3772318.3791278","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3772318.3791278","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3772318.3791278","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049188592","display_name":"Kecheng Lu","orcid":"https://orcid.org/0000-0001-5990-3296"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kecheng Lu","raw_affiliation_strings":["Renmin University of China, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5990-3296","affiliations":[{"raw_affiliation_string":"Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100897474","display_name":"Yue He","orcid":"https://orcid.org/0009-0009-1536-1179"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue He","raw_affiliation_strings":["School of Information, Renmin University of China, Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0009-1536-1179","affiliations":[{"raw_affiliation_string":"School of Information, Renmin University of China, Beijing, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068526974","display_name":"Yunhai Wang","orcid":"https://orcid.org/0000-0003-0059-6580"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunhai Wang","raw_affiliation_strings":["Renmin University of China, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0059-6580","affiliations":[{"raw_affiliation_string":"Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I78988378"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.38580866,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.15700000524520874,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.15700000524520874,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.1387999951839447,"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.12049999833106995,"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/rgb-color-model","display_name":"RGB color model","score":0.572700023651123},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4943000078201294},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.46700000762939453},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.46209999918937683},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4562999904155731},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42579999566078186},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.4068000018596649},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4032000005245209}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6840000152587891},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6614999771118164},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.572700023651123},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4943000078201294},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.46700000762939453},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.46209999918937683},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4562999904155731},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42579999566078186},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4068000018596649},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4032000005245209},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.40130001306533813},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36480000615119934},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3637999892234802},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.34470000863075256},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.34040001034736633},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C2961294","wikidata":"https://www.wikidata.org/wiki/Q166863","display_name":"Color space","level":3,"score":0.29600000381469727},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C186991048","wikidata":"https://www.wikidata.org/wiki/Q1184883","display_name":"Color difference","level":3,"score":0.2630000114440918}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3772318.3791278","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3772318.3791278","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3772318.3791278","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3772318.3791278","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W122142318","https://openalex.org/W1909247982","https://openalex.org/W1964076086","https://openalex.org/W1969048494","https://openalex.org/W1977400967","https://openalex.org/W1992670370","https://openalex.org/W2039583460","https://openalex.org/W2078522365","https://openalex.org/W2079145959","https://openalex.org/W2097121395","https://openalex.org/W2106615295","https://openalex.org/W2133718258","https://openalex.org/W2137167595","https://openalex.org/W2140053249","https://openalex.org/W2162349892","https://openalex.org/W2270878873","https://openalex.org/W2402024572","https://openalex.org/W2404373833","https://openalex.org/W2511469011","https://openalex.org/W2778431694","https://openalex.org/W2793815272","https://openalex.org/W2969297115","https://openalex.org/W2980720999","https://openalex.org/W3091971410","https://openalex.org/W4224999677","https://openalex.org/W4297461942","https://openalex.org/W4309758167","https://openalex.org/W4316591953","https://openalex.org/W4320560534","https://openalex.org/W4324153637","https://openalex.org/W4377995026","https://openalex.org/W4391335485","https://openalex.org/W4396832149","https://openalex.org/W4409736329"],"related_works":[],"abstract_inverted_index":{"Large-scale":[0],"color":[1,23],"datasets":[2],"exhibit":[3],"significant":[4],"sparsity":[5,30],"in":[6,123,133],"name-color":[7],"correspondences,":[8],"substantially":[9],"impeding":[10],"the":[11],"effectiveness":[12],"of":[13],"conventional":[14],"methodologies.":[15],"We":[16,89],"propose":[17],"a":[18,46,56,91],"contrastive":[19,48],"learning-based":[20],"framework":[21,44,71],"for":[22,99,108],"name":[24,59,87],"generation":[25],"and":[26,40,65,85,103,126],"recommendation":[27,39,125],"that":[28],"addresses":[29],"through":[31],"negative":[32,73,86],"sampling,":[33],"supporting":[34],"two":[35],"core":[36],"tasks:":[37],"color-to-name":[38,124],"name-to-color":[41,134],"generation.":[42,135],"Our":[43],"employs":[45],"multi-task":[47],"learning":[49],"architecture":[50],"comprising":[51],"three":[52],"key":[53],"components:":[54],"(1)":[55],"pre-trained":[57],"Transformer-based":[58],"encoder,":[60,64],"(2)":[61],"an":[62,67],"RGB":[63,68,80],"(3)":[66],"generator.":[69],"The":[70],"utilizes":[72],"sampling":[74],"to":[75,131],"construct":[76],"positive-negative":[77],"pairs,":[78],"contrasting":[79],"encoder":[81],"outputs":[82],"with":[83],"positive":[84],"embeddings.":[88],"adopt":[90],"multi-objective":[92],"optimization":[93],"strategy":[94],"incorporating":[95],"binary":[96],"cross-entropy":[97],"loss":[98,107],"neural":[100],"collaborative":[101],"filtering,":[102],"mean":[104],"squared":[105],"error":[106,130],"name-to-RGB":[109],"mapping.":[110],"Experimental":[111],"results":[112],"demonstrate":[113],"substantial":[114],"improvements":[115],"over":[116],"baseline":[117],"methods,":[118],"achieving":[119],"71.26%":[120],"Top-10":[121],"accuracy":[122],"reducing":[127],"CIELAB":[128],"distance":[129],"26.61":[132]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-04-14T00:00:00"}