{"id":"https://openalex.org/W3213804620","doi":"https://doi.org/10.1109/mlsp52302.2021.9596481","title":"GRAD-CAM Guided Channel-Spatial Attention Module for Fine-Grained Visual Classification","display_name":"GRAD-CAM Guided Channel-Spatial Attention Module for Fine-Grained Visual Classification","publication_year":2021,"publication_date":"2021-10-25","ids":{"openalex":"https://openalex.org/W3213804620","doi":"https://doi.org/10.1109/mlsp52302.2021.9596481","mag":"3213804620"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp52302.2021.9596481","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp52302.2021.9596481","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)","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/A5070936827","display_name":"Shuai Xu","orcid":"https://orcid.org/0000-0002-5102-9298"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuai Xu","raw_affiliation_strings":["Pattern Recognition and Intelligent System Lab., Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pattern Recognition and Intelligent System Lab., Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007417490","display_name":"Dongliang Chang","orcid":"https://orcid.org/0000-0002-4081-3001"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongliang Chang","raw_affiliation_strings":["Pattern Recognition and Intelligent System Lab., Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pattern Recognition and Intelligent System Lab., Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048826896","display_name":"Jiyang Xie","orcid":"https://orcid.org/0000-0003-3659-9476"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiyang Xie","raw_affiliation_strings":["Pattern Recognition and Intelligent System Lab., Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pattern Recognition and Intelligent System Lab., Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039812471","display_name":"Zhanyu Ma","orcid":"https://orcid.org/0000-0003-2950-2488"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhanyu Ma","raw_affiliation_strings":["Beijing Academy of Artificial Intelligence, Beijing, China","Pattern Recognition and Intelligent System Lab., Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Academy of Artificial Intelligence, Beijing, China","institution_ids":["https://openalex.org/I4210100255"]},{"raw_affiliation_string":"Pattern Recognition and Intelligent System Lab., Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"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":33,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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":0.9995999932289124,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9970999956130981,"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":0.7120368480682373},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.6551162600517273},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5985986590385437},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5690513253211975},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.530738890171051},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5000438690185547},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4869748055934906},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4540572464466095},{"id":"https://openalex.org/keywords/attention-network","display_name":"Attention network","score":0.4396571218967438},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4115990400314331},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3822157680988312},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.061102449893951416}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7120368480682373},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.6551162600517273},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5985986590385437},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5690513253211975},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.530738890171051},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5000438690185547},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4869748055934906},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4540572464466095},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.4396571218967438},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4115990400314331},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3822157680988312},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.061102449893951416},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp52302.2021.9596481","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp52302.2021.9596481","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5799999833106995,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[{"id":"https://openalex.org/G1216186352","display_name":null,"funder_award_id":"202006470036","funder_id":"https://openalex.org/F4320322725","funder_display_name":"China Scholarship Council"},{"id":"https://openalex.org/G3515233813","display_name":null,"funder_award_id":"61922015,61773071,U19B2036","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6085252604","display_name":null,"funder_award_id":"2020AAA0105200,2019YFF0303300,2019YFF0303302","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G7697810044","display_name":null,"funder_award_id":"Z200002","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1797268635","https://openalex.org/W1846799578","https://openalex.org/W2138011018","https://openalex.org/W2612983688","https://openalex.org/W2737725206","https://openalex.org/W2752782242","https://openalex.org/W2770108382","https://openalex.org/W2798365843","https://openalex.org/W2807931652","https://openalex.org/W2883502031","https://openalex.org/W2884585870","https://openalex.org/W2891951760","https://openalex.org/W2951464224","https://openalex.org/W2951696358","https://openalex.org/W2961018736","https://openalex.org/W2962858109","https://openalex.org/W2963407932","https://openalex.org/W2963420686","https://openalex.org/W2970507129","https://openalex.org/W2986456235","https://openalex.org/W3008809756","https://openalex.org/W3035367622","https://openalex.org/W3108870912","https://openalex.org/W6638677478","https://openalex.org/W6737496325","https://openalex.org/W6753385296","https://openalex.org/W6753412334","https://openalex.org/W6754854709","https://openalex.org/W6767612737"],"related_works":["https://openalex.org/W2068608913","https://openalex.org/W3124914020","https://openalex.org/W4375867731","https://openalex.org/W2141033859","https://openalex.org/W2077542787","https://openalex.org/W2156434174","https://openalex.org/W2071701083","https://openalex.org/W2383687187","https://openalex.org/W2121496884","https://openalex.org/W2387910809"],"abstract_inverted_index":{"Fine-grained":[0],"visual":[1,91],"classification":[2,58],"(FGVC)":[3],"is":[4,88],"becoming":[5],"an":[6],"important":[7],"research":[8],"field,":[9],"due":[10],"to":[11,35,129],"its":[12],"wide":[13],"applications":[14],"and":[15,41,93,131,163],"the":[16,29,38,57,69,73,78,108,123,127,133,138,144,147,170,175,182,186,189,193],"rapid":[17],"development":[18],"of":[19,80,146,181,188],"computer":[20],"vision":[21],"technologies.":[22],"The":[23,50,166],"current":[24],"state-of-the-art":[25],"(SOTA)":[26],"methods":[27],"in":[28,68,174],"FGVC":[30,70,157,176],"usually":[31],"employ":[32],"attention":[33,52,109,120,134,172],"mechanisms":[34],"first":[36],"capture":[37],"semantic":[39],"parts":[40],"then":[42],"discover":[43],"their":[44],"subtle":[45],"differences":[46],"between":[47],"distinct":[48],"classes.":[49],"existing":[51],"modules":[53,173],"have":[54],"significantly":[55],"improved":[56],"performance":[59],"but":[60],"they":[61],"are":[62],"poorly":[63],"guided":[64,118],"since":[65],"part-based":[66],"detectors":[67],"depend":[71],"on":[72,154],"network":[74],"learning":[75],"ability":[76],"without":[77],"supervision":[79],"part":[81,86],"annotations.":[82],"As":[83],"obtaining":[84],"such":[85,96],"annotations":[87],"labor-expensive,":[89],"some":[90],"localization":[92,140],"explanation":[94],"methods,":[95],"as":[97],"gradient-weighted":[98],"class":[99],"activation":[100],"mapping":[101],"(Grad-CAM),":[102],"can":[103],"be":[104],"utilized":[105],"for":[106,122],"supervising":[107],"mechanism.":[110],"In":[111,178],"this":[112],"paper,":[113],"we":[114,150],"propose":[115],"a":[116],"Grad-CAM":[117,128],"channel-spatial":[119],"module":[121],"FGVC,":[124],"which":[125],"employs":[126],"supervise":[130],"constrain":[132],"weights":[135],"by":[136],"generating":[137],"coarse":[139],"maps.":[141],"To":[142],"demonstrate":[143,185],"effectiveness":[145],"proposed":[148,167,190],"method,":[149],"conduct":[151],"comprehensive":[152],"experiments":[153],"three":[155],"popular":[156],"datasets,":[158],"including":[159],"CUB-200-2011,":[160],"Stanford":[161],"Cars,":[162],"FGVC-Aircraft":[164],"datasets.":[165],"method":[168,191],"outperforms":[169],"SOTA":[171,194],"task.":[177],"addition,":[179],"visualizations":[180],"feature":[183],"maps":[184],"superiority":[187],"against":[192],"approaches.":[195]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":6}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}