{"id":"https://openalex.org/W2999511321","doi":"https://doi.org/10.1145/3338533.3366596","title":"Representative Feature Matching Network for Image Retrieval","display_name":"Representative Feature Matching Network for Image Retrieval","publication_year":2019,"publication_date":"2019-12-15","ids":{"openalex":"https://openalex.org/W2999511321","doi":"https://doi.org/10.1145/3338533.3366596","mag":"2999511321"},"language":"en","primary_location":{"id":"doi:10.1145/3338533.3366596","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3338533.3366596","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Multimedia Asia","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/A5006367095","display_name":"Zhuangzi Li","orcid":"https://orcid.org/0000-0002-2565-6810"},"institutions":[{"id":"https://openalex.org/I179026463","display_name":"Beijing Technology and Business University","ror":"https://ror.org/013e0zm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I179026463"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuangzi Li","raw_affiliation_strings":["Beijing Technology and Business University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Technology and Business University, Beijing, China","institution_ids":["https://openalex.org/I179026463"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102887689","display_name":"Feng Dai","orcid":"https://orcid.org/0000-0001-5885-8817"},"institutions":[{"id":"https://openalex.org/I179026463","display_name":"Beijing Technology and Business University","ror":"https://ror.org/013e0zm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I179026463"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Dai","raw_affiliation_strings":["Beijing Technology and Business University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Technology and Business University, Beijing, China","institution_ids":["https://openalex.org/I179026463"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026853565","display_name":"Naiguang Zhang","orcid":"https://orcid.org/0000-0002-7057-4443"},"institutions":[{"id":"https://openalex.org/I4210111085","display_name":"Academy of Broadcasting Science","ror":"https://ror.org/01z4nez64","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210111085"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Naiguang Zhang","raw_affiliation_strings":["Information Technology Institute, Academy of Broadcasting Science, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Technology Institute, Academy of Broadcasting Science, Beijing, China","institution_ids":["https://openalex.org/I4210111085"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100435848","display_name":"Lei Wang","orcid":"https://orcid.org/0000-0002-0961-0441"},"institutions":[{"id":"https://openalex.org/I4210111085","display_name":"Academy of Broadcasting Science","ror":"https://ror.org/01z4nez64","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210111085"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Wang","raw_affiliation_strings":["Information Technology Institute, Academy of Broadcasting Science, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Technology Institute, Academy of Broadcasting Science, Beijing, China","institution_ids":["https://openalex.org/I4210111085"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078021087","display_name":"Ziyu Xue","orcid":"https://orcid.org/0000-0001-7035-7089"},"institutions":[{"id":"https://openalex.org/I4210111085","display_name":"Academy of Broadcasting Science","ror":"https://ror.org/01z4nez64","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210111085"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziyu Xue","raw_affiliation_strings":["Information Technology Institute, Academy of Broadcasting Science, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Technology Institute, Academy of Broadcasting Science, Beijing, China","institution_ids":["https://openalex.org/I4210111085"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1017,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.47638843,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.998199999332428,"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":0.7628832459449768},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7472895383834839},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.7258758544921875},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6746193170547485},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.636458158493042},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5822391510009766},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5765938758850098},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5731799006462097},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.49037671089172363},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.45214730501174927},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.4306083917617798},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4049423933029175},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3531416654586792},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11173436045646667}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7628832459449768},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7472895383834839},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7258758544921875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6746193170547485},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.636458158493042},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5822391510009766},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5765938758850098},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5731799006462097},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.49037671089172363},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.45214730501174927},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.4306083917617798},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4049423933029175},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3531416654586792},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11173436045646667},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3338533.3366596","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3338533.3366596","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Multimedia Asia","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":25,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1910300841","https://openalex.org/W1939575207","https://openalex.org/W1951304353","https://openalex.org/W1974647172","https://openalex.org/W2005720384","https://openalex.org/W2007972815","https://openalex.org/W2074076772","https://openalex.org/W2161969291","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2293597654","https://openalex.org/W2369976370","https://openalex.org/W2739981449","https://openalex.org/W2752782242","https://openalex.org/W2772756366","https://openalex.org/W2798834175","https://openalex.org/W2798956329","https://openalex.org/W2901249224","https://openalex.org/W2918670247","https://openalex.org/W2963276379","https://openalex.org/W3103722964","https://openalex.org/W3123256893","https://openalex.org/W4235375376","https://openalex.org/W4240153047"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W147410782","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W3022252430","https://openalex.org/W4287804464","https://openalex.org/W3103989898","https://openalex.org/W3211292372","https://openalex.org/W2810679507"],"abstract_inverted_index":{"Recent":[0],"convolutional":[1],"neural":[2],"network":[3,67,156],"(CNNs)":[4],"have":[5],"shown":[6],"promising":[7],"performance":[8],"on":[9,117,160,164],"image":[10,70],"retrieval":[11],"due":[12],"to":[13,52,103,122,133],"the":[14,20,31,93,104,118,135,150,169],"powerful":[15],"feature":[16,24,37,45,65,79,86,97,120],"extraction":[17],"capability.":[18],"However,":[19],"potential":[21],"relations":[22,124],"of":[23,95,125,153],"maps":[25,87],"are":[26],"not":[27],"effectively":[28],"exploited":[29,101],"in":[30,35],"before":[32],"CNNs,":[33],"resulting":[34],"inaccurate":[36],"representations.":[38],"To":[39],"address":[40],"this":[41,58],"issue,":[42],"we":[43,60,74,109],"excavate":[44],"channel-wise":[46],"realtions":[47],"by":[48],"a":[49,62,76],"matching":[50,66,80,105,121],"strategy":[51],"adaptively":[53],"highlight":[54,134],"informative":[55],"features.":[56],"In":[57,107],"paper,":[59],"propose":[61,75],"novel":[63,77],"representative":[64,78,90,119],"(RFMN)":[68],"for":[69],"hashing":[71],"retrieval.":[72],"Specifically,":[73],"block":[81],"(RFMB)":[82],"that":[83,145],"can":[84,99,148],"match":[85],"with":[88,128],"their":[89],"one.":[91],"So,":[92],"significance":[94],"each":[96],"map":[98],"be":[100],"according":[102],"similarity.":[106],"addition,":[108],"also":[110],"present":[111],"an":[112],"innovative":[113],"pooling":[114],"layer":[115],"based":[116],"build":[123],"pooled":[126,136],"features":[127,137],"unpooled":[129],"features,":[130],"so":[131],"as":[132],"retained":[138],"more":[139,157],"valuable":[140],"information.":[141],"Extensive":[142],"experiments":[143],"show":[144],"our":[146],"approach":[147],"promote":[149],"average":[151],"results":[152],"conventional":[154],"residual":[155],"than":[158],"2.6%":[159],"Cifar-10":[161],"and":[162],"1.4%":[163],"NUS-WIDE":[165],"dataset,":[166],"meanwhile":[167],"achieve":[168],"state-of-the-art":[170],"performance.":[171]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}