{"id":"https://openalex.org/W4388190915","doi":"https://doi.org/10.1145/3581783.3612458","title":"Local Consensus Enhanced Siamese Network with Reciprocal Loss for Two-view Correspondence Learning","display_name":"Local Consensus Enhanced Siamese Network with Reciprocal Loss for Two-view Correspondence Learning","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4388190915","doi":"https://doi.org/10.1145/3581783.3612458"},"language":"en","primary_location":{"id":"doi:10.1145/3581783.3612458","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612458","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","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/A5114832565","display_name":"Linbo Wang","orcid":"https://orcid.org/0000-0001-7276-7065"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Linbo Wang","raw_affiliation_strings":["Anhui University, Hefei, China"],"affiliations":[{"raw_affiliation_string":"Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114406863","display_name":"Jing Wu","orcid":"https://orcid.org/0000-0002-5272-6167"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Wu","raw_affiliation_strings":["Anhui University, Hefei, China"],"affiliations":[{"raw_affiliation_string":"Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033676752","display_name":"Xianyong Fang","orcid":"https://orcid.org/0000-0002-6045-8430"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianyong Fang","raw_affiliation_strings":["Anhui University, Hefei, China"],"affiliations":[{"raw_affiliation_string":"Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057821256","display_name":"Zhengyi Liu","orcid":"https://orcid.org/0000-0003-3265-823X"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengyi Liu","raw_affiliation_strings":["Anhui University, Hefei, China"],"affiliations":[{"raw_affiliation_string":"Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028833097","display_name":"Chenjie Cao","orcid":"https://orcid.org/0000-0003-3916-2843"},"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":"Chenjie Cao","raw_affiliation_strings":["Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084959430","display_name":"Yanwei Fu","orcid":"https://orcid.org/0000-0002-6595-6893"},"institutions":[{"id":"https://openalex.org/I135237710","display_name":"Zhejiang Normal University","ror":"https://ror.org/01vevwk45","country_code":"CN","type":"education","lineage":["https://openalex.org/I135237710"]},{"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":"Yanwei Fu","raw_affiliation_strings":["Fudan University &amp; Zhejiang Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Fudan University &amp; Zhejiang Normal University, Shanghai, China","institution_ids":["https://openalex.org/I135237710","https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5114832565"],"corresponding_institution_ids":["https://openalex.org/I143868143"],"apc_list":null,"apc_paid":null,"fwci":0.5219,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.7251547,"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":"5235","last_page":"5243"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994999766349792,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","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/T10036","display_name":"Advanced Neural Network Applications","score":0.9987999796867371,"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":0.7098532319068909},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6669352054595947},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6111462712287903},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5833672285079956},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5756450891494751},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5152109265327454},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49839234352111816},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.46845340728759766},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4669811427593231},{"id":"https://openalex.org/keywords/reciprocal","display_name":"Reciprocal","score":0.45534926652908325},{"id":"https://openalex.org/keywords/mutual-information","display_name":"Mutual information","score":0.44026410579681396},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4251728057861328},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.400804340839386},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3817690908908844},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17761865258216858}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7098532319068909},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6669352054595947},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6111462712287903},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5833672285079956},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5756450891494751},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5152109265327454},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49839234352111816},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.46845340728759766},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4669811427593231},{"id":"https://openalex.org/C2777742833","wikidata":"https://www.wikidata.org/wiki/Q1964083","display_name":"Reciprocal","level":2,"score":0.45534926652908325},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.44026410579681396},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4251728057861328},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.400804340839386},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3817690908908844},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17761865258216858},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3581783.3612458","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612458","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3208004626","display_name":null,"funder_award_id":"2108085MF210","funder_id":"https://openalex.org/F4320334897","funder_display_name":"Natural Science Foundation of Anhui Province"}],"funders":[{"id":"https://openalex.org/F4320334897","display_name":"Natural Science Foundation of Anhui Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W59289346","https://openalex.org/W1644644236","https://openalex.org/W1985238052","https://openalex.org/W2085261163","https://openalex.org/W2111073598","https://openalex.org/W2125434107","https://openalex.org/W2126060993","https://openalex.org/W2130017587","https://openalex.org/W2151103935","https://openalex.org/W2166579962","https://openalex.org/W2250384498","https://openalex.org/W2471962767","https://openalex.org/W2740578684","https://openalex.org/W2754925132","https://openalex.org/W2894971516","https://openalex.org/W2963674285","https://openalex.org/W2982933447","https://openalex.org/W2987672160","https://openalex.org/W2990655570","https://openalex.org/W3035563186","https://openalex.org/W3166285241","https://openalex.org/W3204035704","https://openalex.org/W3204057393","https://openalex.org/W4234552385","https://openalex.org/W4283789017","https://openalex.org/W4312322996"],"related_works":["https://openalex.org/W2391753177","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W2996284460","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W4298337043","https://openalex.org/W1966306316","https://openalex.org/W2761785940"],"abstract_inverted_index":{"Recent":[0],"studies":[1],"of":[2,41,148],"two-view":[3],"correspondence":[4,14,46],"learning":[5],"usually":[6],"establish":[7],"an":[8,63,120],"end-to-end":[9],"network":[10,103,139],"to":[11,37,61,116,136],"jointly":[12],"predict":[13],"reliability":[15],"and":[16,58,73,86,110,171],"relative":[17],"pose.":[18],"We":[19,132],"improve":[20],"such":[21],"a":[22,30,45,69,137,141],"framework":[23],"from":[24,128],"two":[25,169],"aspects.":[26],"First,":[27],"we":[28,166],"propose":[29],"Local":[31],"Feature":[32],"Consensus":[33],"(LFC)":[34],"plugin":[35],"block":[36,49],"augment":[38],"the":[39,48,106,117,126,129,146,154,168],"features":[40,53,78,93],"existing":[42,100,134],"models.":[43],"Given":[44],"feature,":[47],"augments":[50],"its":[51],"neighboring":[52],"with":[54,105,140],"mutual":[55,149],"neighborhood":[56],"consensus":[57,82],"aggregates":[59],"them":[60],"produce":[62],"enhanced":[64],"feature.":[65],"As":[66],"inliers":[67],"obey":[68],"uniform":[70],"cross-view":[71],"transformation":[72],"share":[74],"more":[75,94],"consistent":[76],"learned":[77],"than":[79],"outliers,":[80],"feature":[81],"strengthens":[83],"inlier":[84],"correlation":[85],"suppresses":[87],"outlier":[88],"distraction,":[89],"which":[90,151],"makes":[91],"output":[92],"discriminative":[95],"for":[96,119],"classifying":[97],"inliers/outliers.":[98],"Second,":[99],"approaches":[101],"supervise":[102],"training":[104],"ground":[107],"truth":[108],"correspondences":[109],"essential":[111],"matrix":[112],"projecting":[113],"one":[114],"image":[115,122],"other":[118],"input":[121],"pair,":[123],"without":[124,157],"considering":[125],"information":[127],"reverse":[130],"mapping.":[131],"extend":[133],"models":[135],"Siamese":[138],"reciprocal":[142],"loss":[143],"that":[144],"exploits":[145],"supervision":[147],"projection,":[150],"considerably":[152],"promotes":[153],"matching":[155],"performance":[156,175],"introducing":[158],"additional":[159],"model":[160],"parameters.":[161],"Building":[162],"upon":[163],"MSA-Net":[164],"[30],":[165],"implement":[167],"proposals":[170],"experimentally":[172],"achieve":[173],"state-of-the-art":[174],"on":[176],"benchmark":[177],"datasets.":[178]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
