{"id":"https://openalex.org/W2897715967","doi":"https://doi.org/10.1109/ijcnn.2018.8489110","title":"Cross-modal Metric Learning with Graph Embedding","display_name":"Cross-modal Metric Learning with Graph Embedding","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2897715967","doi":"https://doi.org/10.1109/ijcnn.2018.8489110","mag":"2897715967"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2018.8489110","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489110","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5042517423","display_name":"Youcai Zhang","orcid":"https://orcid.org/0000-0003-1967-4091"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Youcai Zhang","raw_affiliation_strings":["Department of Electronic Engineering, Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000141628","display_name":"Xiaodong Gu","orcid":"https://orcid.org/0000-0002-7096-1830"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaodong Gu","raw_affiliation_strings":["Department of Electronic Engineering, Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"47","issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9987000226974487,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9973999857902527,"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/embedding","display_name":"Embedding","score":0.7334743738174438},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6848404407501221},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6005231142044067},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.5544756650924683},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5544332265853882},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.5532377362251282},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5353755354881287},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.48985788226127625},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4763205945491791},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4578056335449219},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4302001893520355},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4241608679294586}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7334743738174438},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6848404407501221},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6005231142044067},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.5544756650924683},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5544332265853882},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.5532377362251282},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5353755354881287},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.48985788226127625},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4763205945491791},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4578056335449219},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4302001893520355},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4241608679294586},{"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},{"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:10.1109/ijcnn.2018.8489110","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489110","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.5400000214576721}],"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":49,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1686810756","https://openalex.org/W1880262756","https://openalex.org/W1895577753","https://openalex.org/W1905882502","https://openalex.org/W1933349210","https://openalex.org/W2007972815","https://openalex.org/W2013535308","https://openalex.org/W2022398331","https://openalex.org/W2053667957","https://openalex.org/W2071207147","https://openalex.org/W2096733369","https://openalex.org/W2100235303","https://openalex.org/W2106277773","https://openalex.org/W2112796928","https://openalex.org/W2117539524","https://openalex.org/W2154851992","https://openalex.org/W2164530430","https://openalex.org/W2211092169","https://openalex.org/W2217869562","https://openalex.org/W2295072214","https://openalex.org/W2326180695","https://openalex.org/W2470322391","https://openalex.org/W2531440880","https://openalex.org/W2604602513","https://openalex.org/W2606965845","https://openalex.org/W2613718673","https://openalex.org/W2623327532","https://openalex.org/W2740474318","https://openalex.org/W2756217268","https://openalex.org/W2766197118","https://openalex.org/W2768454054","https://openalex.org/W2962756421","https://openalex.org/W2963312446","https://openalex.org/W2963389687","https://openalex.org/W2964189431","https://openalex.org/W3099206234","https://openalex.org/W3104097132","https://openalex.org/W4251308012","https://openalex.org/W4294576248","https://openalex.org/W6639619044","https://openalex.org/W6699364125","https://openalex.org/W6701379130","https://openalex.org/W6728374919","https://openalex.org/W6736727294","https://openalex.org/W6738806211","https://openalex.org/W6743830610","https://openalex.org/W6745654871","https://openalex.org/W6745956856"],"related_works":["https://openalex.org/W3036264823","https://openalex.org/W3206528106","https://openalex.org/W2123605750","https://openalex.org/W2912814903","https://openalex.org/W2088740331","https://openalex.org/W2950907416","https://openalex.org/W3038102983","https://openalex.org/W1559483280","https://openalex.org/W2082479932","https://openalex.org/W2932872266"],"abstract_inverted_index":{"Metric":[0],"learning":[1,162],"with":[2,79,136,146,163],"neural":[3,36],"networks":[4],"has":[5],"exhibited":[6],"promising":[7],"improvements":[8],"in":[9,65],"representation":[10,78],"learning.":[11],"Yet":[12],"cross-modal":[13,76,84,185],"retrieval":[14,85],"poses":[15],"a":[16,88,94,132,159],"unique":[17],"challenge":[18],"to":[19,23,41,50,74,139],"metric":[20],"learning:":[21],"how":[22],"compute":[24],"the":[25,52,62,66,83,99,114,125,141,164,170,176,181],"distance":[26],"across":[27],"different":[28],"modalities":[29],"such":[30],"as":[31,87],"image":[32],"and":[33,47,81,104,119,153,172,178],"text.":[34],"Existing":[35],"network":[37],"based":[38],"methods":[39],"tend":[40],"establish":[42],"two":[43],"branches":[44],"for":[45,128,184],"images":[46],"texts":[48],"respectively":[49],"bridge":[51],"modal":[53],"gap.":[54],"Also,":[55],"most":[56],"of":[57,180],"them":[58],"cannot":[59],"fully":[60],"exploit":[61],"structure":[63,129],"embedded":[64],"multimodal":[67,95],"data.":[68],"This":[69],"paper":[70],"introduces":[71],"embedding":[72,90,126,143,183],"layer":[73,127],"provide":[75],"shared":[77],"non-linearity":[80],"reformulates":[82],"problem":[86,91],"graph":[89,100,117,151],"by":[92],"constructing":[93],"graph.":[96,115],"To":[97],"learn":[98],"embedding,":[101],"training":[102],"pairs":[103],"triplets":[105],"are":[106,122,156],"uniformly":[107],"generated":[108],"from":[109],"random":[110],"walk":[111],"sequences":[112],"on":[113,124,169],"Then":[116],"pair":[118,152],"triplet":[120,154],"constraints":[121,155],"imposed":[123],"preservation.":[130],"Meanwhile,":[131],"classifier":[133],"is":[134,144],"trained":[135],"labeled":[137],"data":[138],"ensure":[140],"learned":[142,182],"coupled":[145],"semantic":[147],"information.":[148],"For":[149],"optimization,":[150],"integrated":[157],"into":[158],"unified":[160],"multi-task":[161],"supervised":[165],"classifier.":[166],"Experimental":[167],"results":[168],"Wiki":[171],"NUS-WIDE":[173],"datasets":[174],"demonstrate":[175],"effectiveness":[177],"superiority":[179],"retrieval.":[186]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}