{"id":"https://openalex.org/W2767087869","doi":"https://doi.org/10.1145/3126686.3126726","title":"Towards Improving Canonical Correlation Analysis for Cross-modal Retrieval","display_name":"Towards Improving Canonical Correlation Analysis for Cross-modal Retrieval","publication_year":2017,"publication_date":"2017-10-23","ids":{"openalex":"https://openalex.org/W2767087869","doi":"https://doi.org/10.1145/3126686.3126726","mag":"2767087869"},"language":"en","primary_location":{"id":"doi:10.1145/3126686.3126726","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3126686.3126726","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the on Thematic Workshops of ACM Multimedia 2017","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/A5072350518","display_name":"Jie Shao","orcid":"https://orcid.org/0000-0003-2615-1555"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Shao","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053619472","display_name":"Zhicheng Zhao","orcid":"https://orcid.org/0000-0001-6506-7298"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhicheng Zhao","raw_affiliation_strings":["Beijing University of Posts and Telecommunications &amp; Beijing Key Laboratory of Network System and Network Culture, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications &amp; Beijing Key Laboratory of Network System and Network Culture, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101754632","display_name":"Fei Su","orcid":"https://orcid.org/0000-0003-4245-4687"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Su","raw_affiliation_strings":["Beijing University of Posts and Telecommunications &amp; Beijing Key Laboratory of Network System and Network Culture, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications &amp; Beijing Key Laboratory of Network System and Network Culture, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003505503","display_name":"Ting Yue","orcid":"https://orcid.org/0009-0006-2067-1591"},"institutions":[{"id":"https://openalex.org/I4392021250","display_name":"State Key Laboratory of Networking and Switching Technology","ror":"https://ror.org/00qtv5q45","country_code":null,"type":"facility","lineage":["https://openalex.org/I139759216","https://openalex.org/I4392021250"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Yue","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing, China","institution_ids":["https://openalex.org/I4392021250"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2771,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.65365928,"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":"332","last_page":"339"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","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/T11714","display_name":"Multimodal Machine Learning Applications","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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998000264167786,"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":0.9979000091552734,"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/canonical-correlation","display_name":"Canonical correlation","score":0.8556252717971802},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.6440556645393372},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6386237740516663},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.42514926195144653},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.38549214601516724},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3410763144493103},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2103264331817627},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.07429209351539612},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.058240801095962524}],"concepts":[{"id":"https://openalex.org/C153874254","wikidata":"https://www.wikidata.org/wiki/Q115542","display_name":"Canonical correlation","level":2,"score":0.8556252717971802},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.6440556645393372},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6386237740516663},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.42514926195144653},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.38549214601516724},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3410763144493103},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2103264331817627},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.07429209351539612},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.058240801095962524},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3126686.3126726","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3126686.3126726","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the on Thematic Workshops of ACM Multimedia 2017","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.44999998807907104}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W816459170","https://openalex.org/W1523385540","https://openalex.org/W1583837637","https://openalex.org/W1677182931","https://openalex.org/W1897761818","https://openalex.org/W1905882502","https://openalex.org/W1964073652","https://openalex.org/W1978689620","https://openalex.org/W2007972815","https://openalex.org/W2025768430","https://openalex.org/W2032001302","https://openalex.org/W2045287013","https://openalex.org/W2068042582","https://openalex.org/W2070753207","https://openalex.org/W2100002341","https://openalex.org/W2106277773","https://openalex.org/W2124914669","https://openalex.org/W2130556178","https://openalex.org/W2138118304","https://openalex.org/W2162867699","https://openalex.org/W2163922914","https://openalex.org/W2184188583","https://openalex.org/W2187089797","https://openalex.org/W2326180695","https://openalex.org/W2474425175","https://openalex.org/W2478068293","https://openalex.org/W2524579183","https://openalex.org/W2557865186","https://openalex.org/W4299828299"],"related_works":["https://openalex.org/W2367413540","https://openalex.org/W1968846550","https://openalex.org/W4387164999","https://openalex.org/W2070623039","https://openalex.org/W1991315556","https://openalex.org/W2006749424","https://openalex.org/W2155515508","https://openalex.org/W3131670725","https://openalex.org/W2354853260","https://openalex.org/W1970646221"],"abstract_inverted_index":{"Building":[0],"correlations":[1],"for":[2],"cross-modal":[3],"retrieval,":[4,10],"i.e.,":[5],"image-to-text":[6],"retrieval":[7,60],"and":[8,74,143,164],"text-to-image":[9],"is":[11,53,64,93,197],"a":[12,54,148,194],"feasible":[13],"solution":[14],"to":[15,46,57,66,83,129,141,151,168,199],"bridge":[16],"the":[17,48,59,68,84,87,153,159,174,181,184,201,211,214],"semantic":[18,50,123,132],"gap":[19],"between":[20,71],"different":[21,72],"modalities.":[22],"Canonical":[23],"correlation":[24,70],"analysis":[25],"(CCA)":[26],"based":[27,125],"methods":[28],"have":[29],"ever":[30],"achieved":[31],"great":[32],"successes.":[33],"However,":[34],"conventional":[35],"2-view":[36,140],"CCA":[37,92,112,138,146],"suffers":[38],"from":[39,115,139],"three":[40,116,206],"inherent":[41],"problems:":[42],"1)":[43],"it":[44,63],"fails":[45],"capture":[47],"intra-modal":[49,131],"consistency,":[51],"which":[52],"necessary":[55],"element":[56],"improve":[58,130,200],"performance,":[61],"2)":[62],"hard":[65],"learn":[67],"non-linear":[69],"modalities,":[73],"3)":[75],"there":[76],"exists":[77],"problem":[78],"in":[79,105],"similarity":[80,191,195],"measure":[81],"due":[82],"fact":[85],"that":[86,170],"latent":[88],"space":[89],"learned":[90,179],"by":[91,188],"not":[94],"directly":[95],"optimized":[96],"with":[97],"certain":[98],"distance":[99,202],"measure.":[100,203],"To":[101],"address":[102],"above":[103],"problem,":[104],"this":[106],"paper,":[107],"we":[108,119,135],"propose":[109,120],"an":[110],"improved":[111],"algorithm":[113],"(ICCA)":[114],"aspects.":[117],"First,":[118],"two":[121],"effective":[122],"features":[124,128],"on":[126,205],"text":[127],"consistency.":[133],"Second,":[134],"expand":[136],"traditional":[137],"4-view,":[142],"embed":[144],"4-view":[145],"into":[147],"progressive":[149,156],"framework":[150,157],"alleviate":[152],"over-fitting.":[154],"Our":[155],"combines":[158],"training":[160],"of":[161,173,183,213],"linear":[162],"projection":[163],"nonlinear":[165],"hidden":[166],"layers":[167],"ensure":[169],"good":[171],"representations":[172],"input":[175],"raw":[176],"data":[177,208],"are":[178],"at":[180],"output":[182],"network.":[185],"Third,":[186],"inspired":[187],"large":[189],"scale":[190],"learning":[192],"(LSSL),":[193],"metric":[196],"proposed":[198,215],"Experiments":[204],"publicly":[207],"sets":[209],"demonstrate":[210],"effectiveness":[212],"ICCA":[216],"method.":[217]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
