{"id":"https://openalex.org/W3157950585","doi":"https://doi.org/10.1145/3444685.3446321","title":"Efficient inter-image relation graph neural network hashing for scalable image retrieval","display_name":"Efficient inter-image relation graph neural network hashing for scalable image retrieval","publication_year":2021,"publication_date":"2021-03-07","ids":{"openalex":"https://openalex.org/W3157950585","doi":"https://doi.org/10.1145/3444685.3446321","mag":"3157950585"},"language":"en","primary_location":{"id":"doi:10.1145/3444685.3446321","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3444685.3446321","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd ACM International Conference on Multimedia in Asia","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/A5006987121","display_name":"Hui Cui","orcid":"https://orcid.org/0000-0002-6505-4734"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Cui","raw_affiliation_strings":["Shandong Normal University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Normal University, Jinan, China","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108048954","display_name":"Lei Zhu","orcid":"https://orcid.org/0000-0002-2993-7142"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Zhu","raw_affiliation_strings":["Shandong Normal University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Normal University, Jinan, China","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060463460","display_name":"Wentao Tan","orcid":"https://orcid.org/0000-0001-5907-1581"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wentao Tan","raw_affiliation_strings":["Shandong Normal University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Normal University, Jinan, China","institution_ids":["https://openalex.org/I28006308"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I28006308"],"apc_list":null,"apc_paid":null,"fwci":0.0955,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.30900671,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"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":0.9979000091552734,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9957000017166138,"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":0.7755069732666016},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.7052494287490845},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6940017938613892},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6320643424987793},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.5862781405448914},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5741238594055176},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5658062696456909},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5181185603141785},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5080642700195312},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4177160859107971},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2900705635547638}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7755069732666016},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.7052494287490845},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6940017938613892},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6320643424987793},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.5862781405448914},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5741238594055176},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5658062696456909},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5181185603141785},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5080642700195312},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4177160859107971},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2900705635547638},{"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/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3444685.3446321","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3444685.3446321","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd ACM International Conference on Multimedia in Asia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1861492603","https://openalex.org/W1904365287","https://openalex.org/W1939575207","https://openalex.org/W1974647172","https://openalex.org/W2037227137","https://openalex.org/W2040918046","https://openalex.org/W2074668987","https://openalex.org/W2145065594","https://openalex.org/W2167090521","https://openalex.org/W2282231212","https://openalex.org/W2411707397","https://openalex.org/W2461086877","https://openalex.org/W2509619282","https://openalex.org/W2554234430","https://openalex.org/W2586937979","https://openalex.org/W2594105920","https://openalex.org/W2766280096","https://openalex.org/W2771872884","https://openalex.org/W2781821509","https://openalex.org/W2793456900","https://openalex.org/W2808282156","https://openalex.org/W2904458925","https://openalex.org/W2907166662","https://openalex.org/W2953037339","https://openalex.org/W2962805368","https://openalex.org/W2963323172","https://openalex.org/W2974497444","https://openalex.org/W2981076146","https://openalex.org/W3006683596","https://openalex.org/W3008014442","https://openalex.org/W3035732386","https://openalex.org/W6684945483"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W4205463238","https://openalex.org/W259157601","https://openalex.org/W2761785940","https://openalex.org/W2110523656","https://openalex.org/W1482209366","https://openalex.org/W2521627374"],"abstract_inverted_index":{"Unsupervised":[0],"deep":[1,15,27,37,67,123,198],"hashing":[2,28,61,124,199],"is":[3,42,70,142,165],"a":[4,33,86],"promising":[5],"technique":[6],"for":[7,157],"large-scale":[8],"image":[9,158,175],"retrieval,":[10],"as":[11],"it":[12],"equips":[13],"powerful":[14],"neural":[16,38,68],"networks":[17],"and":[18,115,150,160,179,205],"has":[19],"advantage":[20,65],"on":[21,201],"label":[22],"independence.":[23],"However,":[24],"the":[25,56,64,79,106,121,130,146,170,174,181,185,195],"unsupervised":[26,60,122,197],"process":[29],"needs":[30],"to":[31,44,54,76,119,132,144,167],"train":[32],"large":[34],"amount":[35],"of":[36,59,66,184],"network":[39,69,141],"parameters,":[40],"which":[41,153],"hard":[43],"optimize":[45],"when":[46],"no":[47],"labeled":[48],"training":[49],"samples":[50],"are":[51],"provided.":[52],"How":[53],"maintain":[55],"well":[57],"scalability":[58],"while":[62],"exploiting":[63],"an":[71],"interesting":[72],"but":[73,88],"challenging":[74],"problem":[75],"investigate.":[77],"With":[78],"motivation,":[80],"in":[81],"this":[82],"paper,":[83],"we":[84,104,127],"propose":[85],"simple":[87],"effective":[89],"Inter-image":[90],"Relation":[91],"Graph":[92],"Neural":[93],"Network":[94],"Hashing":[95],"(IRGNNH)":[96],"method.":[97],"Different":[98],"from":[99],"all":[100],"existing":[101],"complex":[102],"models,":[103],"discover":[105],"latent":[107,134],"inter-image":[108,147],"semantic":[109,148],"relations":[110,149,159,172],"without":[111],"any":[112],"manual":[113],"labels":[114],"exploit":[116],"them":[117],"further":[118],"assist":[120],"process.":[125],"Specifically,":[126],"first":[128],"parse":[129],"images":[131],"extract":[133],"involved":[135],"semantics.":[136],"Then,":[137],"relation":[138],"graph":[139],"convolutional":[140],"constructed":[143,171],"model":[145],"visual":[151],"similarity,":[152],"generates":[154],"representation":[155],"vectors":[156],"contents.":[161],"Finally,":[162],"adversarial":[163],"learning":[164,177],"performed":[166],"seamlessly":[168],"embed":[169],"into":[173],"hash":[176,186],"process,":[178],"improve":[180],"discriminative":[182],"capability":[183],"codes.":[187],"Experiments":[188],"demonstrate":[189],"that":[190],"our":[191],"method":[192],"significantly":[193],"outperforms":[194],"state-of-the-art":[196],"methods":[200],"both":[202],"retrieval":[203],"accuracy":[204],"efficiency.":[206]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}