{"id":"https://openalex.org/W1929856797","doi":"https://doi.org/10.1109/cvpr.2015.7298948","title":"MatchNet: Unifying feature and metric learning for patch-based matching","display_name":"MatchNet: Unifying feature and metric learning for patch-based matching","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W1929856797","doi":"https://doi.org/10.1109/cvpr.2015.7298948","mag":"1929856797"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2015.7298948","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298948","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","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/A5064840456","display_name":"Xufeng Han","orcid":null},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xufeng Han","raw_affiliation_strings":["University of North Carolina at Chapel Hill, Chapel Hill, NC, US","University of North Carolina at Chapel, Hill, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of North Carolina at Chapel Hill, Chapel Hill, NC, US","institution_ids":["https://openalex.org/I114027177"]},{"raw_affiliation_string":"University of North Carolina at Chapel, Hill, USA","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111929615","display_name":"Thomas Leung","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Thomas Leung","raw_affiliation_strings":["Google Inc, Mountain View, CA, US","Google Research USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Inc, Mountain View, CA, US","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Google Research USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110220840","display_name":"Yangqing Jia","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yangqing Jia","raw_affiliation_strings":["Google Inc, Mountain View, CA, US","Google Research USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Inc, Mountain View, CA, US","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Google Research USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066107323","display_name":"Rahul Sukthankar","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rahul Sukthankar","raw_affiliation_strings":["Google Inc, Mountain View, CA, US","Google Research USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google Inc, Mountain View, CA, US","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Google Research USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5104361813","display_name":"Alexander C. Berg","orcid":null},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexander C. Berg","raw_affiliation_strings":["University of North Carolina at Chapel Hill, Chapel Hill, NC, US","University of North Carolina at Chapel, Hill, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of North Carolina at Chapel Hill, Chapel Hill, NC, US","institution_ids":["https://openalex.org/I114027177"]},{"raw_affiliation_string":"University of North Carolina at Chapel, Hill, USA","institution_ids":["https://openalex.org/I114027177"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":880,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3279","last_page":"3286"},"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":0.9997000098228455,"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":0.9997000098228455,"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":0.9994000196456909,"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/T10812","display_name":"Human Pose and Action Recognition","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"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.8639578819274902},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7767149209976196},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6571958065032959},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6243671178817749},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5852708220481873},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5788680911064148},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5728812217712402},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5547090768814087},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5486168265342712},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5207790732383728},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4707157015800476},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4088289141654968},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.20888125896453857},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.16978353261947632},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09886631369590759}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8639578819274902},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7767149209976196},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6571958065032959},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6243671178817749},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5852708220481873},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5788680911064148},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5728812217712402},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5547090768814087},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5486168265342712},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5207790732383728},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4707157015800476},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4088289141654968},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.20888125896453857},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.16978353261947632},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09886631369590759},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2015.7298948","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298948","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.697.7049","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.697.7049","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.cmu.edu/%7Erahuls/pub/cvpr2015-matchnet-rahuls.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W205159212","https://openalex.org/W1586283311","https://openalex.org/W1677409904","https://openalex.org/W1953691509","https://openalex.org/W1975517671","https://openalex.org/W1980911747","https://openalex.org/W1995985000","https://openalex.org/W2007178811","https://openalex.org/W2016053056","https://openalex.org/W2068730032","https://openalex.org/W2089888558","https://openalex.org/W2113325037","https://openalex.org/W2117228865","https://openalex.org/W2119525058","https://openalex.org/W2119885577","https://openalex.org/W2124386111","https://openalex.org/W2124404372","https://openalex.org/W2126060993","https://openalex.org/W2126338221","https://openalex.org/W2127589108","https://openalex.org/W2128031471","https://openalex.org/W2129201358","https://openalex.org/W2134514757","https://openalex.org/W2137402913","https://openalex.org/W2144041313","https://openalex.org/W2145072179","https://openalex.org/W2156303437","https://openalex.org/W2157364932","https://openalex.org/W2158602558","https://openalex.org/W2160014001","https://openalex.org/W2163605009","https://openalex.org/W2169561155","https://openalex.org/W2171590421","https://openalex.org/W2177274842","https://openalex.org/W2293597654","https://openalex.org/W2308045930","https://openalex.org/W6608394925","https://openalex.org/W6641037725","https://openalex.org/W6677700107","https://openalex.org/W6679101185","https://openalex.org/W6680343850","https://openalex.org/W6681212791","https://openalex.org/W6681554747","https://openalex.org/W6682864246","https://openalex.org/W6683661426","https://openalex.org/W6684191040","https://openalex.org/W6697214482"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W4378510483","https://openalex.org/W4221142204"],"abstract_inverted_index":{"Motivated":[0],"by":[1,95],"recent":[2],"successes":[3],"on":[4,9,66,118,150],"learning":[5,10],"feature":[6,11,102],"representations":[7],"and":[8,43,69,98,104],"comparison":[12],"functions,":[13],"we":[14,63,88],"propose":[15],"a":[16,24,34,44,53,113],"unified":[17,142],"approach":[18,143],"to":[19,74,121,134],"combining":[20],"both":[21],"for":[22,159],"training":[23,77],"patch":[25,151],"matching":[26,94,152],"system.":[27],"Our":[28,137],"system,":[29],"dubbed":[30],"Match-Net,":[31],"consists":[32],"of":[33,46,116,126,129],"deep":[35],"convolutional":[36],"network":[37,45],"that":[38,51,83,140],"extracts":[39],"features":[40],"from":[41],"patches":[42],"three":[47],"fully":[48],"connected":[49],"layers":[50],"computes":[52],"similarity":[54,105],"between":[55],"the":[56,76,101,124,156],"extracted":[57],"features.":[58],"To":[59],"ensure":[60],"experimental":[61],"repeatability,":[62],"train":[64],"MatchNet":[65,97,164],"standard":[67,119],"datasets":[68,120],"employ":[70],"an":[71],"input":[72],"sampler":[73],"augment":[75],"set":[78,115],"with":[79,131],"synthetic":[80],"exemplar":[81],"pairs":[82],"reduce":[84],"overfitting.":[85],"Once":[86],"trained,":[87],"achieve":[89],"better":[90],"computational":[91],"efficiency":[92],"during":[93],"disassembling":[96],"separately":[99],"applying":[100],"computation":[103],"networks":[106],"in":[107],"two":[108],"sequential":[109],"stages.":[110],"We":[111,161],"perform":[112],"comprehensive":[114],"experiments":[117],"carefully":[122],"study":[123],"contributions":[125],"each":[127],"aspect":[128],"MatchNet,":[130],"direct":[132],"comparisons":[133],"established":[135],"methods.":[136],"results":[138,149],"confirm":[139],"our":[141],"improves":[144],"accuracy":[145],"over":[146],"previous":[147],"state-of-the-art":[148],"datasets,":[153],"while":[154],"reducing":[155],"storage":[157],"requirement":[158],"descriptors.":[160],"make":[162],"pre-trained":[163],"publicly":[165],"available.":[166]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":39},{"year":2024,"cited_by_count":44},{"year":2023,"cited_by_count":65},{"year":2022,"cited_by_count":62},{"year":2021,"cited_by_count":123},{"year":2020,"cited_by_count":112},{"year":2019,"cited_by_count":133},{"year":2018,"cited_by_count":101},{"year":2017,"cited_by_count":117},{"year":2016,"cited_by_count":65},{"year":2015,"cited_by_count":11}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
