{"id":"https://openalex.org/W3090013253","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206611","title":"Unsupervised Deep Imputed Hashing for Partial Cross-modal Retrieval","display_name":"Unsupervised Deep Imputed Hashing for Partial Cross-modal Retrieval","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3090013253","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206611","mag":"3090013253"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9206611","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206611","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/A5100319452","display_name":"Dong Chen","orcid":"https://orcid.org/0000-0002-0526-9346"},"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":"Dong Chen","raw_affiliation_strings":["Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101898805","display_name":"Miaomiao Cheng","orcid":"https://orcid.org/0000-0001-6163-2675"},"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":"Miaomiao Cheng","raw_affiliation_strings":["Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 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, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 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/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":"Liping Jing","raw_affiliation_strings":["Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.1917,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.49562324,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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":1.0,"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":1.0,"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":0.9937999844551086,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9865999817848206,"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":0.7412068843841553},{"id":"https://openalex.org/keywords/binary-code","display_name":"Binary code","score":0.7238044738769531},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.7065263390541077},{"id":"https://openalex.org/keywords/hamming-space","display_name":"Hamming space","score":0.6489310264587402},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6254906058311462},{"id":"https://openalex.org/keywords/dynamic-perfect-hashing","display_name":"Dynamic perfect hashing","score":0.5050600171089172},{"id":"https://openalex.org/keywords/locality-sensitive-hashing","display_name":"Locality-sensitive hashing","score":0.48702675104141235},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.4775013327598572},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47061067819595337},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4667910039424896},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.43840503692626953},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.43195974826812744},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4278469979763031},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4113794267177582},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3820174038410187},{"id":"https://openalex.org/keywords/hash-table","display_name":"Hash table","score":0.3366129398345947},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.32452988624572754},{"id":"https://openalex.org/keywords/hamming-code","display_name":"Hamming code","score":0.25500762462615967},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.22951313853263855},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.22879111766815186},{"id":"https://openalex.org/keywords/double-hashing","display_name":"Double hashing","score":0.18554869294166565},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10899022221565247},{"id":"https://openalex.org/keywords/block-code","display_name":"Block code","score":0.09457170963287354}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7412068843841553},{"id":"https://openalex.org/C63435697","wikidata":"https://www.wikidata.org/wiki/Q864135","display_name":"Binary code","level":3,"score":0.7238044738769531},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.7065263390541077},{"id":"https://openalex.org/C2779494224","wikidata":"https://www.wikidata.org/wiki/Q5645799","display_name":"Hamming space","level":5,"score":0.6489310264587402},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6254906058311462},{"id":"https://openalex.org/C122907437","wikidata":"https://www.wikidata.org/wiki/Q5318999","display_name":"Dynamic perfect hashing","level":5,"score":0.5050600171089172},{"id":"https://openalex.org/C74270461","wikidata":"https://www.wikidata.org/wiki/Q1625299","display_name":"Locality-sensitive hashing","level":4,"score":0.48702675104141235},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.4775013327598572},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47061067819595337},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4667910039424896},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.43840503692626953},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.43195974826812744},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4278469979763031},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4113794267177582},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3820174038410187},{"id":"https://openalex.org/C67388219","wikidata":"https://www.wikidata.org/wiki/Q207440","display_name":"Hash table","level":3,"score":0.3366129398345947},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.32452988624572754},{"id":"https://openalex.org/C73150493","wikidata":"https://www.wikidata.org/wiki/Q853922","display_name":"Hamming code","level":4,"score":0.25500762462615967},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.22951313853263855},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.22879111766815186},{"id":"https://openalex.org/C138111711","wikidata":"https://www.wikidata.org/wiki/Q478351","display_name":"Double hashing","level":4,"score":0.18554869294166565},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10899022221565247},{"id":"https://openalex.org/C157125643","wikidata":"https://www.wikidata.org/wiki/Q884707","display_name":"Block code","level":3,"score":0.09457170963287354},{"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","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},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9206611","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206611","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W199018803","https://openalex.org/W1922199343","https://openalex.org/W2007972815","https://openalex.org/W2049993534","https://openalex.org/W2054141820","https://openalex.org/W2155797815","https://openalex.org/W2161375627","https://openalex.org/W2170942078","https://openalex.org/W2245692474","https://openalex.org/W2251864938","https://openalex.org/W2266728343","https://openalex.org/W2293597654","https://openalex.org/W2337086876","https://openalex.org/W2388114291","https://openalex.org/W2476034201","https://openalex.org/W2519051215","https://openalex.org/W2532171237","https://openalex.org/W2741295496","https://openalex.org/W2795832645","https://openalex.org/W2896986545","https://openalex.org/W2963288100","https://openalex.org/W2964076257","https://openalex.org/W3098232083","https://openalex.org/W6608183366","https://openalex.org/W6683120131","https://openalex.org/W6691091272","https://openalex.org/W6697214482","https://openalex.org/W6721087566","https://openalex.org/W6746719375"],"related_works":["https://openalex.org/W2981938443","https://openalex.org/W2293015666","https://openalex.org/W2158169729","https://openalex.org/W2000284985","https://openalex.org/W2975588143","https://openalex.org/W2565504097","https://openalex.org/W2029205712","https://openalex.org/W4212830455","https://openalex.org/W4297909034","https://openalex.org/W2797819729"],"abstract_inverted_index":{"Cross-modal":[0],"retrieval,":[1],"given":[2],"the":[3,15,43,56,63,73,87,95,125,132,146,151,155,162,168,175,179,194,198],"data":[4,17,58,83,128,185],"of":[5,51,72,89,97,170],"one":[6],"specific":[7],"modality":[8],"as":[9,112],"a":[10,105,120,136],"query,":[11],"aims":[12],"to":[13,29,41,149,177],"search":[14],"relevant":[16],"in":[18,77,86,154],"other":[19],"modalities.":[20],"Recently,":[21],"cross-modal":[22,57,98,108],"hashing":[23,109,152],"has":[24,174],"attracted":[25],"much":[26],"attention":[27],"due":[28],"its":[30],"high":[31],"efficiency":[32],"and":[33,92,182,206],"low":[34],"storage":[35],"cost.":[36],"Its":[37],"main":[38],"idea":[39],"is":[40,59,75,119,129,143,165],"approximate":[42],"cross-modality":[44],"similarity":[45],"via":[46],"binary":[47],"codes.":[48],"This":[49],"kind":[50],"method":[52,196],"works":[53],"well":[54],"when":[55],"completely":[60],"observed.":[61],"However,":[62],"real-world":[64],"application":[65],"usually":[66],"avoids":[67],"this":[68,101],"situation,":[69],"where":[70,161],"part":[71],"information":[74,91],"unobserved":[76,126],"some":[78],"modality.":[79],"Such":[80],"partial":[81],"multimodal":[82],"will":[84],"result":[85],"lack":[88],"pairwise":[90,127],"then":[93],"destroy":[94],"performance":[96],"hashing.":[99],"In":[100],"paper,":[102],"we":[103],"proposed":[104,133,195],"novel":[106],"unsupervised":[107],"approach,":[110],"named":[111],"Unsupervised":[113],"Deep":[114],"Imputed":[115],"Hashing":[116],"(UDIH).":[117],"It":[118],"two-stage":[121],"learning":[122],"strategy.":[123],"Firstly,":[124],"imputed":[130],"by":[131],"generators.":[134],"Then":[135],"neural":[137],"network":[138],"with":[139,167],"weighted":[140],"triplet":[141],"loss":[142],"applied":[144],"on":[145,201],"correlation":[147,163],"graph":[148,164],"learn":[150],"code":[153,210],"Hamming":[156],"space":[157],"for":[158],"each":[159],"modality,":[160],"constructed":[166],"aid":[169],"augmented":[171],"data.":[172],"UDIH":[173],"ability":[176],"preserve":[178],"semantic":[180],"consistency":[181],"difference":[183],"among":[184],"objects.":[186],"The":[187,208],"extensive":[188],"experimental":[189],"results":[190],"have":[191],"shown":[192],"that":[193],"outperforms":[197],"state-of-the-art":[199],"methods":[200],"two":[202],"benchmark":[203],"datasets":[204],"(MIRFlickr":[205],"NUS-WIDE).":[207],"source":[209],"could":[211],"be":[212],"available":[213],"at":[214],"https://github.com/AkChen/UDIH.":[215]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
