{"id":"https://openalex.org/W1934184906","doi":"https://doi.org/10.1109/cvpr.2015.7298958","title":"Recurrent convolutional neural network for object recognition","display_name":"Recurrent convolutional neural network for object recognition","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W1934184906","doi":"https://doi.org/10.1109/cvpr.2015.7298958","mag":"1934184906"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2015.7298958","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298958","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/A5021434848","display_name":"Ming Liang","orcid":"https://orcid.org/0000-0002-2723-9041"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Liang","raw_affiliation_strings":["State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Computer Science and Technology Center for Brain-Inspired Computing Research (CBICR) Tsinghua University, Beijing, China","State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing 100084, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Computer Science and Technology Center for Brain-Inspired Computing Research (CBICR) Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing 100084, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004579631","display_name":"Xiaolin Hu","orcid":"https://orcid.org/0000-0002-4907-7354"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolin Hu","raw_affiliation_strings":["State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Computer Science and Technology Center for Brain-Inspired Computing Research (CBICR) Tsinghua University, Beijing, China","State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing 100084, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Computer Science and Technology Center for Brain-Inspired Computing Research (CBICR) Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing 100084, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1021,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3367","last_page":"3375"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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/T10036","display_name":"Advanced Neural Network Applications","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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9991000294685364,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.8438792824745178},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8087397813796997},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7427400350570679},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6756053566932678},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6548517942428589},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.6135124564170837},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6110914349555969},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.5950343608856201},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5227949023246765},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5144917964935303},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5066776871681213},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5027499198913574},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.44087520241737366},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.42510825395584106},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3691314160823822},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3533700406551361}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.8438792824745178},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8087397813796997},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7427400350570679},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6756053566932678},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6548517942428589},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.6135124564170837},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6110914349555969},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.5950343608856201},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5227949023246765},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5144917964935303},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5066776871681213},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5027499198913574},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.44087520241737366},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.42510825395584106},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3691314160823822},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3533700406551361},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr.2015.7298958","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298958","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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":88,"referenced_works":["https://openalex.org/W4919037","https://openalex.org/W78356000","https://openalex.org/W1423339008","https://openalex.org/W1546771929","https://openalex.org/W1601963269","https://openalex.org/W1686810756","https://openalex.org/W1694178301","https://openalex.org/W1827297289","https://openalex.org/W1959983357","https://openalex.org/W1966476349","https://openalex.org/W1995341919","https://openalex.org/W1997585233","https://openalex.org/W2008188887","https://openalex.org/W2008353316","https://openalex.org/W2011815965","https://openalex.org/W2044613381","https://openalex.org/W2062118960","https://openalex.org/W2082007860","https://openalex.org/W2090042335","https://openalex.org/W2095705004","https://openalex.org/W2096723260","https://openalex.org/W2097117768","https://openalex.org/W2101926813","https://openalex.org/W2103212315","https://openalex.org/W2107215754","https://openalex.org/W2108598243","https://openalex.org/W2108806771","https://openalex.org/W2110485445","https://openalex.org/W2112796928","https://openalex.org/W2116360511","https://openalex.org/W2122585011","https://openalex.org/W2123716044","https://openalex.org/W2125663122","https://openalex.org/W2128084896","https://openalex.org/W2130325614","https://openalex.org/W2138558323","https://openalex.org/W2143612262","https://openalex.org/W2144219012","https://openalex.org/W2147800946","https://openalex.org/W2149194912","https://openalex.org/W2150355110","https://openalex.org/W2154579312","https://openalex.org/W2156387975","https://openalex.org/W2163605009","https://openalex.org/W2168894214","https://openalex.org/W2170942820","https://openalex.org/W2294059674","https://openalex.org/W2335728318","https://openalex.org/W2504108613","https://openalex.org/W2585198072","https://openalex.org/W2950179405","https://openalex.org/W2951287198","https://openalex.org/W2962835968","https://openalex.org/W2963025229","https://openalex.org/W2963173190","https://openalex.org/W2963574257","https://openalex.org/W2963828527","https://openalex.org/W2963911037","https://openalex.org/W3118608800","https://openalex.org/W3207342693","https://openalex.org/W4254816979","https://openalex.org/W4297791040","https://openalex.org/W6600213771","https://openalex.org/W6603161775","https://openalex.org/W6628124331","https://openalex.org/W6632360823","https://openalex.org/W6637373629","https://openalex.org/W6637551671","https://openalex.org/W6638444622","https://openalex.org/W6638685980","https://openalex.org/W6639736908","https://openalex.org/W6648737282","https://openalex.org/W6674330103","https://openalex.org/W6674914833","https://openalex.org/W6676231585","https://openalex.org/W6676258337","https://openalex.org/W6676297131","https://openalex.org/W6682279845","https://openalex.org/W6682751323","https://openalex.org/W6684191040","https://openalex.org/W6684278770","https://openalex.org/W6684665197","https://openalex.org/W6696761078","https://openalex.org/W6703116779","https://openalex.org/W6787972765","https://openalex.org/W6814477976","https://openalex.org/W7023653924","https://openalex.org/W7053011049"],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W2886711096","https://openalex.org/W3036048022","https://openalex.org/W4309224979","https://openalex.org/W3026879719","https://openalex.org/W4366089167","https://openalex.org/W2969228573","https://openalex.org/W2963690996","https://openalex.org/W2552919655"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"the":[3,26,30,45,74,78,88,96,105,108,112,127,145,154,178,187,199,202],"convolutional":[4,71],"neural":[5,124],"network":[6,137,147],"(CNN)":[7],"has":[8,148],"achieved":[9],"great":[10],"success":[11],"in":[12,44,133],"many":[13,23],"computer":[14],"vision":[15],"tasks.":[16],"Partially":[17],"inspired":[18],"by":[19,53,65,95],"neuroscience,":[20],"CNN":[21,37,60],"shares":[22],"properties":[24],"with":[25,138],"visual":[27,46],"system":[28,47],"of":[29,80,90,98,107,142,183,189,201],"brain.":[31],"A":[32],"prominent":[33],"difference":[34],"is":[35,38,76,93,116,159],"that":[36,87],"typically":[39],"a":[40,58,139],"feed-forward":[41,207],"architecture":[42],"while":[43],"recurrent":[48,59,67,123,203],"connections":[49,68],"are":[50],"abundant.":[51],"Inspired":[52],"this":[54],"fact,":[55],"we":[56],"propose":[57],"(RCNN)":[61],"for":[62,118,209],"object":[63,119,164,210],"recognition":[64,165],"incorporating":[66],"into":[69],"each":[70,91],"layer.":[72],"Though":[73],"input":[75],"static,":[77],"activities":[79,97],"RCNN":[81,128,176],"units":[82],"evolve":[83],"over":[84,205],"time":[85,130],"so":[86],"activity":[89],"unit":[92],"modulated":[94],"its":[99],"neighboring":[100],"units.":[101],"This":[102],"property":[103],"enhances":[104],"ability":[106],"model":[109,158],"to":[110,192],"integrate":[111],"context":[113],"information,":[114],"which":[115,151],"important":[117],"recognition.":[120,211],"Like":[121],"other":[122],"networks,":[125],"unfolding":[126],"through":[129],"can":[131,152],"result":[132],"an":[134],"arbitrarily":[135],"deep":[136],"fixed":[140],"number":[141,188],"parameters.":[143],"Furthermore,":[144],"unfolded":[146],"multiple":[149],"paths,":[150],"facilitate":[153],"learning":[155],"process.":[156],"The":[157],"tested":[160],"on":[161,181],"four":[162],"benchmark":[163],"datasets:":[166],"CIFAR-10,":[167],"CIFAR-100,":[168],"MNIST":[169],"and":[170],"SVHN.":[171],"With":[172],"fewer":[173],"trainable":[174],"parameters,":[175],"outperforms":[177],"state-of-the-art":[179],"models":[180],"all":[182],"these":[184],"datasets.":[185],"Increasing":[186],"parameters":[190],"leads":[191],"even":[193],"better":[194],"performance.":[195],"These":[196],"results":[197],"demonstrate":[198],"advantage":[200],"structure":[204,208],"purely":[206]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":48},{"year":2024,"cited_by_count":54},{"year":2023,"cited_by_count":91},{"year":2022,"cited_by_count":79},{"year":2021,"cited_by_count":124},{"year":2020,"cited_by_count":150},{"year":2019,"cited_by_count":154},{"year":2018,"cited_by_count":123},{"year":2017,"cited_by_count":107},{"year":2016,"cited_by_count":70},{"year":2015,"cited_by_count":9},{"year":2014,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}