{"id":"https://openalex.org/W3163725569","doi":"https://doi.org/10.1109/access.2021.3079435","title":"Multi-Attention Ghost Residual Fusion Network for Image Classification","display_name":"Multi-Attention Ghost Residual Fusion Network for Image Classification","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3163725569","doi":"https://doi.org/10.1109/access.2021.3079435","mag":"3163725569"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3079435","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3079435","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2021.3079435","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101088490","display_name":"Xiaofen Jia","orcid":"https://orcid.org/0000-0002-1891-7613"},"institutions":[{"id":"https://openalex.org/I184681353","display_name":"Anhui University of Science and Technology","ror":"https://ror.org/00q9atg80","country_code":"CN","type":"education","lineage":["https://openalex.org/I184681353"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaofen Jia","raw_affiliation_strings":["School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China","State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China","institution_ids":["https://openalex.org/I184681353"]},{"raw_affiliation_string":"State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan, China","institution_ids":["https://openalex.org/I184681353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036413014","display_name":"Shengjie Du","orcid":null},"institutions":[{"id":"https://openalex.org/I184681353","display_name":"Anhui University of Science and Technology","ror":"https://ror.org/00q9atg80","country_code":"CN","type":"education","lineage":["https://openalex.org/I184681353"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengjie Du","raw_affiliation_strings":["School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China","institution_ids":["https://openalex.org/I184681353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034622807","display_name":"Yongcun Guo","orcid":"https://orcid.org/0000-0001-5199-4146"},"institutions":[{"id":"https://openalex.org/I184681353","display_name":"Anhui University of Science and Technology","ror":"https://ror.org/00q9atg80","country_code":"CN","type":"education","lineage":["https://openalex.org/I184681353"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongcun Guo","raw_affiliation_strings":["State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan, China","institution_ids":["https://openalex.org/I184681353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031647208","display_name":"Yourui Huang","orcid":"https://orcid.org/0000-0002-9774-5791"},"institutions":[{"id":"https://openalex.org/I184681353","display_name":"Anhui University of Science and Technology","ror":"https://ror.org/00q9atg80","country_code":"CN","type":"education","lineage":["https://openalex.org/I184681353"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yourui Huang","raw_affiliation_strings":["School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China","institution_ids":["https://openalex.org/I184681353"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036289370","display_name":"Baiting Zhao","orcid":"https://orcid.org/0000-0002-3325-6737"},"institutions":[{"id":"https://openalex.org/I184681353","display_name":"Anhui University of Science and Technology","ror":"https://ror.org/00q9atg80","country_code":"CN","type":"education","lineage":["https://openalex.org/I184681353"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baiting Zhao","raw_affiliation_strings":["School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China"],"raw_orcid":"https://orcid.org/0000-0002-3325-6737","affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China","institution_ids":["https://openalex.org/I184681353"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I184681353"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.4983,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.67732717,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"9","issue":null,"first_page":"81421","last_page":"81431"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9986000061035156,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/residual","display_name":"Residual","score":0.7569369077682495},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6521061658859253},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6458084583282471},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6447578072547913},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5972436666488647},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5939334034919739},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5275067090988159},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.5220775008201599},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5164151787757874},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.46850305795669556},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.4617479145526886},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.45672401785850525},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.26255667209625244},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2357911467552185},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.22971758246421814},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.213677316904068}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7569369077682495},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6521061658859253},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6458084583282471},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6447578072547913},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5972436666488647},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5939334034919739},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5275067090988159},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.5220775008201599},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5164151787757874},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.46850305795669556},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.4617479145526886},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.45672401785850525},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.26255667209625244},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2357911467552185},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.22971758246421814},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.213677316904068},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2021.3079435","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3079435","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:2b5f1ed392a04fb6b37af2878aba38af","is_oa":true,"landing_page_url":"https://doaj.org/article/2b5f1ed392a04fb6b37af2878aba38af","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 81421-81431 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3079435","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3079435","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2558360764","display_name":null,"funder_award_id":"61501006","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4731596324","display_name":null,"funder_award_id":"2016YFC0600908","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2194775991","https://openalex.org/W2541317894","https://openalex.org/W2549139847","https://openalex.org/W2612445135","https://openalex.org/W2618530766","https://openalex.org/W2884585870","https://openalex.org/W2896409484","https://openalex.org/W2903637075","https://openalex.org/W2951569836","https://openalex.org/W2954826145","https://openalex.org/W2963125010","https://openalex.org/W2963163009","https://openalex.org/W2963363373","https://openalex.org/W2963420686","https://openalex.org/W2963446712","https://openalex.org/W2963918968","https://openalex.org/W2964478996","https://openalex.org/W2971019574","https://openalex.org/W2979936711","https://openalex.org/W2980843098","https://openalex.org/W2982083293","https://openalex.org/W2988812296","https://openalex.org/W3008689640","https://openalex.org/W3013269789","https://openalex.org/W3016986506","https://openalex.org/W3021594706","https://openalex.org/W3034552520","https://openalex.org/W3035414587","https://openalex.org/W3039013989","https://openalex.org/W3083066014","https://openalex.org/W3086104743","https://openalex.org/W3087810439","https://openalex.org/W3089272862","https://openalex.org/W3092033856","https://openalex.org/W3092871726","https://openalex.org/W3097702032","https://openalex.org/W3113223504","https://openalex.org/W3117420249","https://openalex.org/W3175343851","https://openalex.org/W4295131367","https://openalex.org/W4297775537","https://openalex.org/W4306169305","https://openalex.org/W6637373629","https://openalex.org/W6728948280","https://openalex.org/W6737664043","https://openalex.org/W6755843862","https://openalex.org/W6774570552","https://openalex.org/W6776784337","https://openalex.org/W6780019567","https://openalex.org/W6787750877","https://openalex.org/W6842543250","https://openalex.org/W6845574989"],"related_works":["https://openalex.org/W3107204728","https://openalex.org/W4287591324","https://openalex.org/W3108503355","https://openalex.org/W3090555870","https://openalex.org/W4226420367","https://openalex.org/W2748667022","https://openalex.org/W4229443568","https://openalex.org/W3211770882","https://openalex.org/W2010629964","https://openalex.org/W2983135586"],"abstract_inverted_index":{"In":[0],"order":[1],"to":[2,47,129,143],"achieve":[3,109],"high-efficiency":[4],"and":[5,34,43,84,95,98,108,133,157,179,195],"high-precision":[6,145],"multi-image":[7],"classification":[8,36,165,189,212,233],"tasks,":[9],"a":[10,59,81,115,160],"multi-attention":[11],"ghost":[12,29,72,86,93,102],"residual":[13,30,73,87,96],"fusion":[14],"network":[15,27,32,37],"(MAGR)":[16],"is":[17,20,64,78,114,206,214,227,235],"proposed.":[18],"MAGR":[19,50,186,203,224],"formed":[21,65],"by":[22,66,80,169,208,216,229,237],"cascading":[23,67],"basic":[24],"feature":[25,137,155],"extraction":[26],"(BFE),":[28],"mapping":[31],"(GRM)":[33],"image":[35,57],"(IC).":[38],"The":[39,62,90,112,147],"BFE":[40],"uses":[41],"spatial":[42],"channel":[44,154,173],"attention":[45,174],"mechanisms":[46],"help":[48],"the":[49,55,99,150,164,171,200,211,221,232],"extract":[51,130,134],"low-level":[52],"features":[53,132],"of":[54,101,152,167,202,223],"input":[56],"in":[58,163],"targeted":[60],"manner.":[61],"GRM":[63,125],"4":[68],"multi-branch":[69],"group":[70],"convolutional":[71],"blocks":[74],"(MGR-Blocks).":[75],"Each":[76],"MGR-Block":[77],"cascaded":[79],"dimension":[82],"reducer":[83],"several":[85],"sub-networks":[88],"(GRSs).":[89],"GRS":[91,113],"integrates":[92],"convolution":[94,103,117],"connection,":[97],"use":[100],"can":[104],"significantly":[105],"reduce":[106],"parameters":[107,201,222],"high-efficient":[110],"classification.":[111,146],"parallel":[116],"structure":[118],"with":[119,198,219],"32":[120],"branches,":[121],"which":[122],"ensures":[123],"that":[124,185],"has":[126,187],"enough":[127],"width":[128],"advanced":[131],"as":[135,139,142],"much":[136],"information":[138],"possible,":[140],"so":[141],"obtain":[144],"IC":[148],"completes":[149],"aggregation":[151],"high-dimensional":[153],"information,":[156],"then":[158],"achieves":[159],"significant":[161],"improvement":[162],"accuracy":[166,213,234],"MAGR,":[168],"fusing":[170],"effective":[172],"mechanism,":[175],"global":[176],"average":[177],"pooling":[178],"SoftMax":[180],"layer.":[181],"Simulation":[182],"experiment":[183],"shows":[184],"excellent":[188],"capability":[190],"while":[191,210,231],"achieving":[192],"high":[193],"efficiency":[194],"lightweight.":[196],"Compare":[197,218],"VGG16,":[199],"on":[204,225],"CIFAR-10":[205],"reduced":[207,228],"94.8%":[209],"increased":[215,236],"1.18%.":[217],"MobileNetV2,":[220],"CIFAR-100":[226],"33.9%":[230],"15.6%.":[238]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}