{"id":"https://openalex.org/W4416016881","doi":"https://doi.org/10.1145/3746252.3761019","title":"KALE: Knowledge Aggregation for Label-free Model Enhancement","display_name":"KALE: Knowledge Aggregation for Label-free Model Enhancement","publication_year":2025,"publication_date":"2025-11-07","ids":{"openalex":"https://openalex.org/W4416016881","doi":"https://doi.org/10.1145/3746252.3761019"},"language":null,"primary_location":{"id":"doi:10.1145/3746252.3761019","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746252.3761019","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","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":null,"display_name":"Yuebin Xu","orcid":"https://orcid.org/0009-0006-3119-5121"},"institutions":[{"id":"https://openalex.org/I4210159029","display_name":"Guangzhou HKUST Fok Ying Tung Research Institute","ror":"https://ror.org/05cvbj479","country_code":"CN","type":"facility","lineage":["https://openalex.org/I200769079","https://openalex.org/I4210159029"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuebin Xu","raw_affiliation_strings":["HKUST (Guangzhou), Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0009-0006-3119-5121","affiliations":[{"raw_affiliation_string":"HKUST (Guangzhou), Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I4210159029"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004379782","display_name":"Xuemei Peng","orcid":"https://orcid.org/0000-0002-2705-2877"},"institutions":[{"id":"https://openalex.org/I4210159029","display_name":"Guangzhou HKUST Fok Ying Tung Research Institute","ror":"https://ror.org/05cvbj479","country_code":"CN","type":"facility","lineage":["https://openalex.org/I200769079","https://openalex.org/I4210159029"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuemei Peng","raw_affiliation_strings":["HKUST (Guangzhou), Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0002-2705-2877","affiliations":[{"raw_affiliation_string":"HKUST (Guangzhou), Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I4210159029"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091307961","display_name":"Z Chen","orcid":"https://orcid.org/0009-0008-1852-7229"},"institutions":[{"id":"https://openalex.org/I4210159029","display_name":"Guangzhou HKUST Fok Ying Tung Research Institute","ror":"https://ror.org/05cvbj479","country_code":"CN","type":"facility","lineage":["https://openalex.org/I200769079","https://openalex.org/I4210159029"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyi Chen","raw_affiliation_strings":["HKUST (Guangzhou), Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0009-0008-1852-7229","affiliations":[{"raw_affiliation_string":"HKUST (Guangzhou), Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I4210159029"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013127195","display_name":"Zeyi Wen","orcid":"https://orcid.org/0000-0003-3370-6053"},"institutions":[{"id":"https://openalex.org/I4210159029","display_name":"Guangzhou HKUST Fok Ying Tung Research Institute","ror":"https://ror.org/05cvbj479","country_code":"CN","type":"facility","lineage":["https://openalex.org/I200769079","https://openalex.org/I4210159029"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zeyi Wen","raw_affiliation_strings":["HKUST (Guangzhou), Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0003-3370-6053","affiliations":[{"raw_affiliation_string":"HKUST (Guangzhou), Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I4210159029"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210159029"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36831343,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3698","last_page":"3707"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.47189998626708984,"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":0.47189998626708984,"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":0.28040000796318054,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.0575999990105629,"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/entropy","display_name":"Entropy (arrow of time)","score":0.5045999884605408},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4918999969959259},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.43779999017715454},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.4235999882221222},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.40459999442100525},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.38199999928474426},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.3783999979496002},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3538999855518341}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7878999710083008},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6021000146865845},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5314000248908997},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5059999823570251},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5045999884605408},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4918999969959259},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.43779999017715454},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.4235999882221222},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.40459999442100525},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.38199999928474426},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3783999979496002},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3538999855518341},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.31949999928474426},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3109999895095825},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.2840000092983246},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2761000096797943},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.26010000705718994},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.2531000077724457}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746252.3761019","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746252.3761019","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5352819258","display_name":null,"funder_award_id":"2025A1515010261","funder_id":"https://openalex.org/F4320321921","funder_display_name":"Natural Science Foundation of Guangdong Province"},{"id":"https://openalex.org/G8389337031","display_name":null,"funder_award_id":"62306256","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation 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/F4320321921","display_name":"Natural Science Foundation of Guangdong Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1977766639","https://openalex.org/W2047643928","https://openalex.org/W2138011018","https://openalex.org/W2592962403","https://openalex.org/W2964194231","https://openalex.org/W3156892778","https://openalex.org/W3173162544","https://openalex.org/W3174697615","https://openalex.org/W3198675127","https://openalex.org/W4306317014","https://openalex.org/W4306317334","https://openalex.org/W4313156423","https://openalex.org/W4367174885","https://openalex.org/W4402500936","https://openalex.org/W4403582640","https://openalex.org/W4406472599","https://openalex.org/W4415795533"],"related_works":[],"abstract_inverted_index":{"Large":[0],"foundation":[1],"models":[2,17],"have":[3],"demonstrated":[4],"remarkable":[5],"success":[6],"in":[7,24,154,193],"natural":[8],"language":[9],"processing":[10],"and":[11,40,85,107,111,115,172,189,211,229,233],"computer":[12],"vision.":[13],"Applying":[14],"the":[15,28,32,135,138,151,217],"large":[16],"to":[18,26,133,149,200],"downstream":[19],"tasks":[20],"often":[21],"requires":[22],"fine-tuning,":[23,192],"order":[25],"boost":[27],"predictive":[29],"accuracy.":[30],"However,":[31],"fine-tuning":[33,46,226],"process":[34],"relies":[35],"heavily":[36],"on":[37,165,216],"labeled":[38],"data":[39],"extensive":[41],"training.":[42],"This":[43],"dependency":[44],"makes":[45],"impractical":[47],"for":[48,69,76,158],"niche":[49],"applications,":[50],"such":[51],"as":[52],"rare":[53],"object":[54],"detection":[55],"or":[56],"specialized":[57],"medical":[58],"tasks.":[59],"To":[60],"overcome":[61],"these":[62],"limitations,":[63],"we":[64,126],"propose":[65],"KALE:":[66],"Knowledge":[67],"Aggregation":[68],"Label-free":[70],"model":[71,77,83],"Enhancement,":[72],"a":[73,94,128,144,155],"label-free":[74,180],"method":[75,90,164,181,203,222],"enhancement,":[78],"leveraging":[79],"knowledge":[80],"aggregation":[81],"via":[82],"fusion":[84,139,152],"adaptive":[86],"representation":[87,146],"alignment.":[88],"Our":[89],"is":[91],"powered":[92],"by":[93,122,227],"carefully":[95],"designed":[96],"joint":[97],"self-cooperative":[98],"optimization":[99,105],"function":[100],"that":[101,178],"considers":[102],"(i)":[103],"multi-granularity":[104],"(task-specific":[106],"layer-specific),":[108],"(ii)":[109],"self":[110],"cooperative":[112],"supervision":[113],"integration,":[114],"(iii)":[116],"mitigation":[117],"of":[118,137,195,207],"error":[119],"accumulation":[120],"caused":[121],"entropy":[123],"minimization.":[124],"Additionally,":[125],"introduce":[127],"class":[129],"cardinality-aware":[130],"sample":[131],"filtering":[132],"ensure":[134],"stability":[136],"process.":[140],"We":[141,161],"also":[142],"design":[143],"lightweight":[145],"alignment":[147],"technique":[148],"refine":[150],"coefficient":[153],"few":[156],"shots":[157],"quality":[159],"enhancement.":[160],"evaluate":[162],"our":[163,179,202,221],"multiple":[166],"image":[167],"classification":[168],"datasets":[169],"using":[170],"ViT-B/32":[171,210,232],"ViT-L/14":[173],"backbones.":[174],"Experimental":[175],"results":[176],"demonstrate":[177],"consistently":[182],"outperforms":[183],"state-of-the-art":[184],"unsupervised":[185],"approaches,":[186],"including":[187],"TURTLE":[188],"supervised":[190,224],"full":[191,225],"terms":[194],"average":[196,205],"performance.":[197],"Specifically,":[198],"compared":[199],"TURTLE,":[201],"achieves":[204],"improvements":[206],"20.7%":[208],"with":[209,213,231],"19.5%":[212],"ViT-L/14.":[214],"Furthermore,":[215],"challenging":[218],"SUN397":[219],"dataset,":[220],"surpasses":[223],"4%":[228],"2.3%":[230],"ViT-L/14,":[234],"respectively.":[235]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-11-08T00:00:00"}