{"id":"https://openalex.org/W2082741089","doi":"https://doi.org/10.1145/2647868.2654993","title":"Modeling Attributes from Category-Attribute Proportions","display_name":"Modeling Attributes from Category-Attribute Proportions","publication_year":2014,"publication_date":"2014-11-03","ids":{"openalex":"https://openalex.org/W2082741089","doi":"https://doi.org/10.1145/2647868.2654993","mag":"2082741089"},"language":"en","primary_location":{"id":"doi:10.1145/2647868.2654993","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2647868.2654993","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd 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/A5110132392","display_name":"Felix X. Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Felix X. Yu","raw_affiliation_strings":["Columbia University, New York, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Columbia University, New York, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103187717","display_name":"Liangliang Cao","orcid":"https://orcid.org/0000-0003-0900-1512"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liangliang Cao","raw_affiliation_strings":["IBM, Yorktown Heights, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM, Yorktown Heights, NY, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068061267","display_name":"Michele Merler","orcid":"https://orcid.org/0000-0002-4358-8671"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michele Merler","raw_affiliation_strings":["IBM, Yorktown Heights, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM, Yorktown Heights, NY, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079673164","display_name":"Noel Codella","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Noel Codella","raw_affiliation_strings":["IBM, Yorktown Heights, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM, Yorktown Heights, NY, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100357735","display_name":"Tao Chen","orcid":"https://orcid.org/0000-0002-1797-0359"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tao Chen","raw_affiliation_strings":["Columbia University, New York, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Columbia University, New York, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105523625","display_name":"John R. Smith","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"John R. Smith","raw_affiliation_strings":["IBM, Yorktown Heights, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM, Yorktown Heights, NY, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037340457","display_name":"Shih\u2010Fu Chang","orcid":"https://orcid.org/0000-0003-1444-1205"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shih-Fu Chang","raw_affiliation_strings":["Columbia University, New York, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Columbia University, New York, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7305,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.87945257,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"977","last_page":"980"},"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":0.9983999729156494,"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":0.9983999729156494,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.998199999332428,"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":0.9975000023841858,"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/interpretability","display_name":"Interpretability","score":0.7596253156661987},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7052407264709473},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6807345747947693},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6350129246711731},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5306172370910645},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4796783924102783},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4775698781013489},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46808892488479614},{"id":"https://openalex.org/keywords/attribute-domain","display_name":"Attribute domain","score":0.46186500787734985},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.44034916162490845},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.42399734258651733},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3498695492744446},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21769219636917114},{"id":"https://openalex.org/keywords/rough-set","display_name":"Rough set","score":0.06581151485443115}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7596253156661987},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7052407264709473},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6807345747947693},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6350129246711731},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5306172370910645},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4796783924102783},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4775698781013489},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46808892488479614},{"id":"https://openalex.org/C75814411","wikidata":"https://www.wikidata.org/wiki/Q4818714","display_name":"Attribute domain","level":3,"score":0.46186500787734985},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.44034916162490845},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.42399734258651733},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3498695492744446},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21769219636917114},{"id":"https://openalex.org/C111012933","wikidata":"https://www.wikidata.org/wiki/Q3137210","display_name":"Rough set","level":2,"score":0.06581151485443115},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2647868.2654993","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2647868.2654993","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd ACM international conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306116","display_name":"U.S. Department of the Interior","ror":"https://ror.org/03v0pmy70"},{"id":"https://openalex.org/F4320307762","display_name":"International Business Machines Corporation","ror":"https://ror.org/05hh8d621"},{"id":"https://openalex.org/F4320333452","display_name":"Interior Business Center","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1563294799","https://openalex.org/W1995318441","https://openalex.org/W2004236981","https://openalex.org/W2018668305","https://openalex.org/W2063438554","https://openalex.org/W2064851185","https://openalex.org/W2070148066","https://openalex.org/W2075456404","https://openalex.org/W2085660690","https://openalex.org/W2098986246","https://openalex.org/W2124805727","https://openalex.org/W2134270519","https://openalex.org/W2136507825","https://openalex.org/W2157032868","https://openalex.org/W2163811596","https://openalex.org/W2181800684","https://openalex.org/W2185563688","https://openalex.org/W2406675335","https://openalex.org/W2536626143","https://openalex.org/W3143107425","https://openalex.org/W6684348295"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W4390569940","https://openalex.org/W2888392564","https://openalex.org/W4361193272","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W2806259446","https://openalex.org/W2963326959","https://openalex.org/W4312407344","https://openalex.org/W2894289927"],"abstract_inverted_index":{"Attribute-based":[0],"representation":[1],"has":[2],"been":[3],"widely":[4],"used":[5],"in":[6,91],"visual":[7,180],"recognition":[8],"and":[9,15,34,161,183],"retrieval":[10],"due":[11],"to":[12,42,147],"its":[13],"interpretability":[14],"cross-category":[16],"generalization":[17],"properties.":[18],"However,":[19],"classic":[20],"attribute":[21,55,99],"learning":[22,100,106,121],"requires":[23],"manually":[24,133],"labeling":[25,134],"attributes":[26,44,53],"on":[27,57,74,116],"the":[28,58,84,87,92,96,126,149,168],"images,":[29,135],"which":[30],"is":[31,114,129,138,171],"very":[32],"expensive,":[33],"not":[35,140],"scalable.":[36],"In":[37],"this":[38],"paper,":[39],"we":[40,69],"propose":[41,146],"model":[43,52,70],"from":[45,107,151],"category-attribute":[46,77,127],"proportions.":[47],"The":[48,98,165],"proposed":[49,119,169],"framework":[50],"can":[51,101],"without":[54],"labels":[56],"images.":[59],"Specifically,":[60],"given":[61],"a":[62,105,117,141],"multi-class":[63],"image":[64],"datasets":[65],"with":[66],"N":[67],"categories,":[68],"an":[71,75],"attribute,":[72],"based":[73,115],"N-dimensional":[76],"proportion":[78,88,109],"vector,":[79],"where":[80],"each":[81],"element":[82],"of":[83,89,167],"vector":[85],"characterizes":[86],"images":[90],"corresponding":[93],"category":[94],"having":[95],"attribute.":[97],"be":[102],"formulated":[103],"as":[104,155],"label":[108],"(LLP)":[110],"problem.":[111],"Our":[112],"method":[113],"newly":[118],"machine":[120],"algorithm":[122],"called":[123],"$\\propto$SVM.":[124],"Finding":[125],"proportions":[128,150],"much":[130],"easier":[131],"than":[132],"but":[136],"it":[137],"still":[139],"trivial":[142],"task.":[143],"We":[144],"further":[145],"estimate":[148],"multiple":[152],"modalities":[153],"such":[154],"human":[156],"commonsense":[157],"knowledge,":[158],"NLP":[159],"tools,":[160],"other":[162],"domain":[163],"knowledge.":[164],"value":[166],"approach":[170],"demonstrated":[172],"by":[173],"various":[174],"applications":[175],"including":[176],"modeling":[177],"animal":[178],"attributes,":[179,182],"sentiment":[181],"scene":[184],"attributes.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}