{"id":"https://openalex.org/W3187148564","doi":"https://doi.org/10.1145/3462244.3479892","title":"Efficient Deep Feature Calibration for Cross-Modal Joint Embedding Learning","display_name":"Efficient Deep Feature Calibration for Cross-Modal Joint Embedding Learning","publication_year":2021,"publication_date":"2021-10-15","ids":{"openalex":"https://openalex.org/W3187148564","doi":"https://doi.org/10.1145/3462244.3479892","mag":"3187148564"},"language":"en","primary_location":{"id":"doi:10.1145/3462244.3479892","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3462244.3479892","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 International Conference on Multimodal Interaction","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2108.00705","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102947045","display_name":"Zhongwei Xie","orcid":"https://orcid.org/0000-0001-6346-0707"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]},{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["CN","US"],"is_corresponding":true,"raw_author_name":"Zhongwei Xie","raw_affiliation_strings":["School of Computer Science, Georgia Institute of Technology, USA and School of Computer Science and Technology, Wuhan University of Technology, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Georgia Institute of Technology, USA and School of Computer Science and Technology, Wuhan University of Technology, China","institution_ids":["https://openalex.org/I130701444","https://openalex.org/I196699116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100343991","display_name":"Ling Liu","orcid":"https://orcid.org/0000-0002-4138-3082"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ling Liu","raw_affiliation_strings":["School of Computer Science, Georgia Institute of Technology, USA"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Georgia Institute of Technology, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100412815","display_name":"Lin Li","orcid":"https://orcid.org/0000-0001-7553-6916"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Li","raw_affiliation_strings":["School of Computer Science and Technology, Wuhan University of Technology, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Wuhan University of Technology, China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037897744","display_name":"Luo Zhong","orcid":"https://orcid.org/0000-0002-2346-9659"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Luo Zhong","raw_affiliation_strings":["School of Computer Science and Technology, Wuhan University of Technology, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Wuhan University of Technology, China","institution_ids":["https://openalex.org/I196699116"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5102947045"],"corresponding_institution_ids":["https://openalex.org/I130701444","https://openalex.org/I196699116"],"apc_list":null,"apc_paid":null,"fwci":0.1938,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.486352,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"43","last_page":"51"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9991999864578247,"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":0.9991999864578247,"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":0.9988999962806702,"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/T10028","display_name":"Topic Modeling","score":0.989799976348877,"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/computer-science","display_name":"Computer science","score":0.7691665887832642},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7281566858291626},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6076692938804626},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6013373136520386},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5577975511550903},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5400200486183167},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5369443893432617},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5125417709350586},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5114304423332214},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.47324711084365845},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.47115030884742737},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4476798176765442},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.25664693117141724}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7691665887832642},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7281566858291626},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6076692938804626},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6013373136520386},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5577975511550903},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5400200486183167},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5369443893432617},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5125417709350586},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5114304423332214},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.47324711084365845},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.47115030884742737},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4476798176765442},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25664693117141724},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3462244.3479892","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3462244.3479892","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 International Conference on Multimodal Interaction","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2108.00705","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2108.00705","pdf_url":"https://arxiv.org/pdf/2108.00705","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2108.00705","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2108.00705","pdf_url":"https://arxiv.org/pdf/2108.00705","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7200000286102295}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W12634471","https://openalex.org/W1522301498","https://openalex.org/W1525595230","https://openalex.org/W1603598191","https://openalex.org/W1686810756","https://openalex.org/W1949478088","https://openalex.org/W1978394996","https://openalex.org/W2099471712","https://openalex.org/W2153579005","https://openalex.org/W2194775991","https://openalex.org/W2264685884","https://openalex.org/W2598634450","https://openalex.org/W2737041163","https://openalex.org/W2765440071","https://openalex.org/W2896457183","https://openalex.org/W2897152025","https://openalex.org/W2948037078","https://openalex.org/W2950577311","https://openalex.org/W2950726992","https://openalex.org/W2962879692","https://openalex.org/W2963055199","https://openalex.org/W2963997278","https://openalex.org/W2964137095","https://openalex.org/W2965373594","https://openalex.org/W2978017171","https://openalex.org/W3034303366","https://openalex.org/W3035032757"],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W3125011624","https://openalex.org/W1508631387","https://openalex.org/W2370917603","https://openalex.org/W2952760143","https://openalex.org/W2017776670","https://openalex.org/W2786094008","https://openalex.org/W3131501806","https://openalex.org/W2799683370","https://openalex.org/W2807745940"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"a":[3],"two-phase":[4],"deep":[5,23,51,56,124,159],"feature":[6,24,52,57,125,160],"calibration":[7,25,53,126,161],"framework":[8],"for":[9,40,82],"efficient":[10],"learning":[11],"of":[12,106],"semantics":[13,101],"enhanced":[14],"text-image":[15,66],"cross-modal":[16],"joint":[17,32,115,119],"embedding,":[18],"which":[19],"clearly":[20],"separates":[21],"the":[22,31,37,41,87,98,107,114,129,142,158,164],"in":[26,113],"data":[27],"preprocessing":[28],"from":[29,64],"training":[30],"embedding":[33,120],"model.":[34],"We":[35,69,91],"use":[36],"Recipe1M":[38],"dataset":[39],"technical":[42],"description":[43],"and":[44,96,110,136,146],"empirical":[45],"validation.":[46],"In":[47,118],"preprocessing,":[48],"we":[49,122],"perform":[50,123],"by":[54,127],"combining":[55],"engineering":[58],"with":[59,134,157],"semantic":[60,104],"context":[61],"features":[62],"derived":[63],"raw":[65],"input":[67],"data.":[68],"leverage":[70,92],"LSTM":[71],"to":[72,78,94,102],"identify":[73],"key":[74,83,88],"terms,":[75],"NLP":[76],"methods":[77],"produce":[79],"ranking":[80],"scores":[81],"terms":[84],"before":[85],"generating":[86],"term":[89],"feature.":[90],"wideResNet50":[93],"extract":[95],"encode":[97],"image":[99,111],"category":[100],"help":[103],"alignment":[105,144,148],"learned":[108],"recipe":[109],"embeddings":[112],"latent":[116],"space.":[117],"learning,":[121],"optimizing":[128],"batch-hard":[130],"triplet":[131],"loss":[132,145],"function":[133],"soft-margin":[135],"double":[137],"negative":[138],"sampling,":[139],"also":[140],"utilizing":[141],"category-based":[143],"discriminator-based":[147],"loss.":[149],"Extensive":[150],"experiments":[151],"demonstrate":[152],"that":[153],"our":[154],"SEJE":[155],"approach":[156],"significantly":[162],"outperforms":[163],"state-of-the-art":[165],"approaches.":[166]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}