{"id":"https://openalex.org/W2980381887","doi":"https://doi.org/10.1145/3340555.3353752","title":"Unsupervised Deep Fusion Cross-modal Hashing","display_name":"Unsupervised Deep Fusion Cross-modal Hashing","publication_year":2019,"publication_date":"2019-10-14","ids":{"openalex":"https://openalex.org/W2980381887","doi":"https://doi.org/10.1145/3340555.3353752","mag":"2980381887"},"language":"en","primary_location":{"id":"doi:10.1145/3340555.3353752","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3340555.3353752","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Multimodal Interaction","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/A5100673241","display_name":"Jiaming Huang","orcid":"https://orcid.org/0009-0008-4993-760X"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiaming Huang","raw_affiliation_strings":["Beijing Jiaotong University, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105839565","display_name":"Chen Min","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Min","raw_affiliation_strings":["Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069749738","display_name":"Liping Jing","orcid":"https://orcid.org/0000-0001-7578-3407"},"institutions":[{"id":"https://openalex.org/I4210162623","display_name":"Datang Telecom Group (China)","ror":"https://ror.org/050rf2257","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210162623"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liping Jing","raw_affiliation_strings":["Beijing Key Lab of Traffic Data Analysis and Mining, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Traffic Data Analysis and Mining, China","institution_ids":["https://openalex.org/I4210162623"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100673241"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.1012,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.4471759,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"358","last_page":"366"},"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":0.9998999834060669,"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":0.9998999834060669,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.996399998664856,"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":0.9908999800682068,"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/computer-science","display_name":"Computer science","score":0.71442711353302},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.6990668773651123},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.5410425066947937},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4714582562446594},{"id":"https://openalex.org/keywords/universal-hashing","display_name":"Universal hashing","score":0.4445464015007019},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4224485754966736},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3923952579498291},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3549685478210449},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.34875670075416565},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3350905179977417},{"id":"https://openalex.org/keywords/hash-table","display_name":"Hash table","score":0.31905531883239746},{"id":"https://openalex.org/keywords/double-hashing","display_name":"Double hashing","score":0.2924731373786926}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.71442711353302},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.6990668773651123},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.5410425066947937},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4714582562446594},{"id":"https://openalex.org/C116058348","wikidata":"https://www.wikidata.org/wiki/Q846912","display_name":"Universal hashing","level":5,"score":0.4445464015007019},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4224485754966736},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3923952579498291},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3549685478210449},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.34875670075416565},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3350905179977417},{"id":"https://openalex.org/C67388219","wikidata":"https://www.wikidata.org/wiki/Q207440","display_name":"Hash table","level":3,"score":0.31905531883239746},{"id":"https://openalex.org/C138111711","wikidata":"https://www.wikidata.org/wiki/Q478351","display_name":"Double hashing","level":4,"score":0.2924731373786926},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3340555.3353752","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3340555.3353752","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Multimodal Interaction","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4773637693","display_name":null,"funder_award_id":"61822601, 61773050, 61632004","funder_id":"https://openalex.org/F4320327720","funder_display_name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320327720","display_name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1861492603","https://openalex.org/W1964073652","https://openalex.org/W1970055505","https://openalex.org/W1976258951","https://openalex.org/W2007972815","https://openalex.org/W2049993534","https://openalex.org/W2086958058","https://openalex.org/W2155803963","https://openalex.org/W2266728343","https://openalex.org/W2337086876","https://openalex.org/W2411707397","https://openalex.org/W2418353079","https://openalex.org/W2519051215","https://openalex.org/W2526152041","https://openalex.org/W2808243243","https://openalex.org/W2889024947","https://openalex.org/W2894874917","https://openalex.org/W2913932916","https://openalex.org/W2963187862","https://openalex.org/W3098232083"],"related_works":["https://openalex.org/W2132034299","https://openalex.org/W3124800526","https://openalex.org/W4251536634","https://openalex.org/W2088296667","https://openalex.org/W1551533237","https://openalex.org/W2144265691","https://openalex.org/W2185281229","https://openalex.org/W1750955152","https://openalex.org/W2811247857","https://openalex.org/W4385261619"],"abstract_inverted_index":{"To":[0],"handle":[1],"the":[2,37,44,50,90,99,130,135,150,177,178,190,197,215,220],"large-scale":[3],"data":[4,131],"in":[5,23,61,146],"terms":[6],"of":[7,46,52,92,181,206],"storage":[8],"and":[9,21,49,86,96,104,199],"searching":[10],"time,":[11],"learning":[12],"to":[13,18,35,41,133,140,148,188],"hash":[14,155,192],"becomes":[15],"popular":[16],"due":[17],"its":[19],"efficiency":[20],"effectiveness":[22],"approximate":[24],"cross-modal":[25,32,77,117,225],"nearest":[26],"neighbors":[27],"searching.":[28],"Most":[29],"existing":[30],"unsupervised":[31,116],"hashing":[33,78,118],"methods,":[34],"shorten":[36],"semantic":[38],"gap,":[39],"try":[40],"simultaneously":[42,67],"minimize":[43],"loss":[45,51],"intra-modal":[47,85,179],"similarity":[48,88,106],"inter-modal":[53,87,105],"similarity.":[54],"However,":[55],"these":[56,63],"models":[57],"can":[58],"not":[59],"guarantee":[60],"theory":[62],"two":[64,169],"losses":[65],"are":[66],"minimized.":[68],"In":[69,110],"this":[70,111],"paper,":[71],"we":[72,113,153],"first":[73,172],"theoretically":[74],"proved":[75],"that":[76,101,214],"could":[79],"be":[80],"implemented":[81],"by":[82,194],"protecting":[83],"both":[84],"with":[89],"aid":[91],"variational":[93],"inference":[94],"technique":[95],"point":[97],"out":[98],"problem":[100],"maximizing":[102],"intra":[103],"is":[107],"mutually":[108],"constrained.":[109],"case,":[112],"propose":[114],"an":[115],"framework":[119,167,218],"named":[120],"as":[121],"Unsupervised":[122],"Deep":[123],"Fusion":[124],"Cross-modal":[125],"Hashing":[126],"(UDFCH)":[127],"which":[128],"leverages":[129],"fusion":[132],"capture":[134],"underlying":[136],"manifold":[137,200],"across":[138],"modalities":[139],"avoid":[141],"above":[142],"problem.":[143],"What\u2019s":[144],"more,":[145],"order":[147],"reduce":[149],"quantization":[151],"loss,":[152],"sample":[154],"codes":[156],"from":[157],"different":[158,224],"Bernoulli":[159],"distributions":[160],"through":[161],"a":[162],"reparameterization":[163],"trick.":[164],"Our":[165],"UDFCH":[166,217],"has":[168],"stages.":[170],"The":[171,184],"stage":[173,186],"aims":[174,187],"at":[175],"mining":[176],"structure":[180,201],"each":[182],"modality.":[183],"second":[185],"determine":[189],"modality-aware":[191],"code":[193],"sufficiently":[195],"considering":[196],"correlation":[198],"among":[202],"modalities.":[203],"A":[204],"series":[205],"experiments":[207],"conducted":[208],"on":[209,223],"three":[210],"benchmark":[211],"datasets":[212],"show":[213],"proposed":[216],"outperforms":[219],"state-of-the-art":[221],"methods":[222],"retrieval":[226],"tasks.":[227]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}