{"id":"https://openalex.org/W4313055655","doi":"https://doi.org/10.1145/3548608.3559211","title":"Teeth attention mechanism image classification based on lightweight network","display_name":"Teeth attention mechanism image classification based on lightweight network","publication_year":2022,"publication_date":"2022-06-24","ids":{"openalex":"https://openalex.org/W4313055655","doi":"https://doi.org/10.1145/3548608.3559211"},"language":"en","primary_location":{"id":"doi:10.1145/3548608.3559211","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3548608.3559211","pdf_url":null,"source":{"id":"https://openalex.org/S4363608876","display_name":"Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics","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/A5100715303","display_name":"Zejun Zhang","orcid":"https://orcid.org/0000-0003-1113-0532"},"institutions":[{"id":"https://openalex.org/I83791580","display_name":"Fujian University of Technology","ror":"https://ror.org/03c8fdb16","country_code":"CN","type":"education","lineage":["https://openalex.org/I83791580"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zejun Zhang","raw_affiliation_strings":["School of Electronics, Electrical and Physics, Fujian University of Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics, Electrical and Physics, Fujian University of Technology, China","institution_ids":["https://openalex.org/I83791580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085700380","display_name":"Yulong He","orcid":"https://orcid.org/0009-0005-5323-2352"},"institutions":[{"id":"https://openalex.org/I83791580","display_name":"Fujian University of Technology","ror":"https://ror.org/03c8fdb16","country_code":"CN","type":"education","lineage":["https://openalex.org/I83791580"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yulong He","raw_affiliation_strings":["School of Electronics, Electrical and Physics, Fujian University of Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics, Electrical and Physics, Fujian University of Technology, China","institution_ids":["https://openalex.org/I83791580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100953923","display_name":"Huabin He","orcid":null},"institutions":[{"id":"https://openalex.org/I83791580","display_name":"Fujian University of Technology","ror":"https://ror.org/03c8fdb16","country_code":"CN","type":"education","lineage":["https://openalex.org/I83791580"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huabin He","raw_affiliation_strings":["School of Electronics, Electrical and Physics, Fujian University of Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics, Electrical and Physics, Fujian University of Technology, China","institution_ids":["https://openalex.org/I83791580"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070124104","display_name":"Zhiming Cai","orcid":"https://orcid.org/0000-0003-0149-165X"},"institutions":[{"id":"https://openalex.org/I83791580","display_name":"Fujian University of Technology","ror":"https://ror.org/03c8fdb16","country_code":"CN","type":"education","lineage":["https://openalex.org/I83791580"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiming Cai","raw_affiliation_strings":["School of Electronics, Electrical and Physics, Fujian University of Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics, Electrical and Physics, Fujian University of Technology, China","institution_ids":["https://openalex.org/I83791580"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I83791580"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16472676,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"300","last_page":"305"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9904000163078308,"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":0.9904000163078308,"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":0.9527000188827515,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9402999877929688,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.7978123426437378},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6513327956199646},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.6242123246192932},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6168121099472046},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5856175422668457},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5668443441390991},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5667598247528076},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5358980894088745},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5358916521072388},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5251013040542603},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5148736238479614},{"id":"https://openalex.org/keywords/chamfer","display_name":"Chamfer (geometry)","score":0.44387441873550415},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.44235560297966003},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4414594769477844},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.43842241168022156},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4188283085823059},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4150836169719696},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3736332058906555},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.34726792573928833},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2523597478866577},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.15684381127357483},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.0986211895942688},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.07435667514801025}],"concepts":[{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.7978123426437378},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6513327956199646},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.6242123246192932},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6168121099472046},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5856175422668457},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5668443441390991},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5667598247528076},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5358980894088745},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5358916521072388},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5251013040542603},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5148736238479614},{"id":"https://openalex.org/C2776313386","wikidata":"https://www.wikidata.org/wiki/Q18018756","display_name":"Chamfer (geometry)","level":2,"score":0.44387441873550415},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.44235560297966003},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4414594769477844},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.43842241168022156},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4188283085823059},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4150836169719696},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3736332058906555},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34726792573928833},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2523597478866577},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.15684381127357483},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0986211895942688},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.07435667514801025},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3548608.3559211","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3548608.3559211","pdf_url":null,"source":{"id":"https://openalex.org/S4363608876","display_name":"Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2412782625","https://openalex.org/W2618530766"],"related_works":["https://openalex.org/W2088651759","https://openalex.org/W2134508677","https://openalex.org/W4324094550","https://openalex.org/W2054153498","https://openalex.org/W2953234277","https://openalex.org/W4381747464","https://openalex.org/W4307266377","https://openalex.org/W2995343971","https://openalex.org/W2982536526","https://openalex.org/W2992221004"],"abstract_inverted_index":{"In":[0,53,86],"order":[1],"to":[2,83,108,111],"solve":[3],"the":[4,18,54,87,91,102,109,126,135,173],"problem":[5],"that":[6],"embedding":[7],"attention":[8,49,56,89],"mechanism":[9],"module":[10,164],"into":[11,78,125],"lightweight":[12,35,127],"convolutional":[13],"neural":[14],"network":[15,36],"will":[16],"increase":[17,171],"number":[19,174],"of":[20,47,97,172,175],"parameters":[21,176],"and":[22,50,66,74,80,94,131,134,148,157,177],"FLOPs":[23],"with":[24,146,169],"less":[25,170],"accuracy":[26,168],"improvement,":[27],"a":[28,119],"Teeth":[29],"Attention":[30,154,160],"Module":[31,150,155,161],"(TAM)":[32],"based":[33],"on":[34,140],"was":[37,123,137],"proposed.":[38],"The":[39],"feature":[40,103],"map":[41,104],"is":[42,61,76,116],"enhanced":[43],"from":[44,64],"two":[45],"aspects":[46],"channel":[48,55,59],"spatial":[51,88],"attention.":[52],"module,":[57,90],"local":[58],"interaction":[60],"carried":[62,138],"out":[63,139],"height":[65],"width":[67],"without":[68],"dimensionality":[69],"reduction":[70],"through":[71],"one-dimensional":[72],"convolution,":[73],"it":[75],"divided":[77],"MP-ECAM":[79],"AP-ECAM":[81],"according":[82,107],"pooling":[84],"mode.":[85],"average":[92],"value":[93,96],"maximum":[95],"each":[98],"pixel":[99],"point":[100],"in":[101,118],"are":[105],"calculated":[106],"dimensions":[110],"construct":[112],"ESAM.":[113],"Finally,":[114],"TAM":[115,122],"constructed":[117],"channel-space-channel":[120],"manner.":[121],"embedded":[124],"networks":[128],"MobileNetV2,":[129],"ShuffleNetV2":[130],"EfficientNetV1":[132],"respectively,":[133],"experiment":[136],"CIFAR-10":[141],"image":[142],"classification":[143],"datasets.":[144],"Compared":[145],"Squeeze":[147],"Excitation":[149],"(SE),":[151],"Convolutional":[152],"Block":[153],"(CBAM)":[156],"Efficient":[158],"Channel":[159],"(ECA),":[162],"this":[163],"can":[165],"achieve":[166],"higher":[167],"FLOPs.":[178]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
