{"id":"https://openalex.org/W4226408079","doi":"https://doi.org/10.1109/access.2022.3163302","title":"Weakly Supervised Semantic and Attentive Data Mixing Augmentation for Fine-Grained Visual Categorization","display_name":"Weakly Supervised Semantic and Attentive Data Mixing Augmentation for Fine-Grained Visual Categorization","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4226408079","doi":"https://doi.org/10.1109/access.2022.3163302"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3163302","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3163302","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09745100.pdf","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://ieeexplore.ieee.org/ielx7/6287639/9668973/09745100.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012718035","display_name":"Mengqi He","orcid":"https://orcid.org/0000-0003-1096-0040"},"institutions":[{"id":"https://openalex.org/I118347636","display_name":"Australian National University","ror":"https://ror.org/019wvm592","country_code":"AU","type":"education","lineage":["https://openalex.org/I118347636"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Mengqi He","raw_affiliation_strings":["College of Engineering and Computer Science, The Australian National University, Canberra, ACT, Australia"],"raw_orcid":"https://orcid.org/0000-0003-1096-0040","affiliations":[{"raw_affiliation_string":"College of Engineering and Computer Science, The Australian National University, Canberra, ACT, Australia","institution_ids":["https://openalex.org/I118347636"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030906076","display_name":"Qilong Cheng","orcid":"https://orcid.org/0000-0002-2669-4253"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Qilong Cheng","raw_affiliation_strings":["Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada"],"raw_orcid":"https://orcid.org/0000-0002-2669-4253","affiliations":[{"raw_affiliation_string":"Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada","institution_ids":["https://openalex.org/I185261750"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006116691","display_name":"Guanqiu Qi","orcid":"https://orcid.org/0000-0001-9562-3865"},"institutions":[{"id":"https://openalex.org/I115441956","display_name":"Buffalo State University","ror":"https://ror.org/05ms04m92","country_code":"US","type":"education","lineage":["https://openalex.org/I115441956"]},{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guanqiu Qi","raw_affiliation_strings":["Computer Information Systems Department, The State University of New York at Buffalo State, Buffalo, NY, USA"],"raw_orcid":"https://orcid.org/0000-0001-9562-3865","affiliations":[{"raw_affiliation_string":"Computer Information Systems Department, The State University of New York at Buffalo State, Buffalo, NY, USA","institution_ids":["https://openalex.org/I115441956","https://openalex.org/I63190737"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.6479,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.64790556,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"10","issue":null,"first_page":"35814","last_page":"35823"},"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":0.9994999766349792,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9983999729156494,"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":0.8486766815185547},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.795534610748291},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6644080281257629},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.654057502746582},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.46605974435806274},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4591282308101654},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4588812291622162},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4539094865322113},{"id":"https://openalex.org/keywords/mixing","display_name":"Mixing (physics)","score":0.43641749024391174},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.43606358766555786},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42943498492240906},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.16975358128547668}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8486766815185547},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.795534610748291},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6644080281257629},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.654057502746582},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.46605974435806274},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4591282308101654},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4588812291622162},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4539094865322113},{"id":"https://openalex.org/C138777275","wikidata":"https://www.wikidata.org/wiki/Q6884054","display_name":"Mixing (physics)","level":2,"score":0.43641749024391174},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.43606358766555786},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42943498492240906},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.16975358128547668},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3163302","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3163302","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09745100.pdf","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:273a9507172d444a98cafe05b347e438","is_oa":true,"landing_page_url":"https://doaj.org/article/273a9507172d444a98cafe05b347e438","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 10, Pp 35814-35823 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3163302","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3163302","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09745100.pdf","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":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4226408079.pdf","grobid_xml":"https://content.openalex.org/works/W4226408079.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W1797268635","https://openalex.org/W1846799578","https://openalex.org/W2117539524","https://openalex.org/W2138011018","https://openalex.org/W2295107390","https://openalex.org/W2460852148","https://openalex.org/W2746314669","https://openalex.org/W2765407302","https://openalex.org/W2773003563","https://openalex.org/W2783482415","https://openalex.org/W2798365843","https://openalex.org/W2807931652","https://openalex.org/W2891951760","https://openalex.org/W2953915809","https://openalex.org/W2961018736","https://openalex.org/W2963031676","https://openalex.org/W2963407932","https://openalex.org/W2963542991","https://openalex.org/W2992308087","https://openalex.org/W2998508940","https://openalex.org/W3117510992","https://openalex.org/W3124951096","https://openalex.org/W3126558081","https://openalex.org/W3175248300","https://openalex.org/W4288622677","https://openalex.org/W6629368666","https://openalex.org/W6638319203","https://openalex.org/W6638677478","https://openalex.org/W6743428213","https://openalex.org/W6747939174","https://openalex.org/W6759000249","https://openalex.org/W6764782982"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4297676672","https://openalex.org/W4281702477","https://openalex.org/W4378510483","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W4221142204"],"abstract_inverted_index":{"As":[0],"a":[1,44,79,104,151],"key":[2],"factor,":[3],"the":[4,11,17,52,73,96,126,147,169,189,214,217],"availability":[5],"of":[6,13,19,51,98,107,128,139,216],"large-scale":[7],"training":[8,35,65,74,100,130,165,174,186],"samples":[9,66,187],"determines":[10],"improvement":[12],"visual":[14],"performance.":[15],"However,":[16],"size":[18],"Fine-Grained":[20],"Visual":[21],"Categorization":[22],"(FGVC)":[23],"datasets":[24],"is":[25,43,119,195],"always":[26],"limited.":[27],"Therefore,":[28],"overfitting":[29],"as":[30],"an":[31],"issue":[32],"in":[33,69],"FGVC-related":[34],"needs":[36],"to":[37,91,113,146,162,168,182],"be":[38],"solved.":[39],"Data":[40,88],"mixing":[41,56,81],"augmentation":[42,47,57,82],"widely-used":[45],"data":[46,55,80],"method.":[48],"In":[49,102],"most":[50],"recently":[53],"proposed":[54,193,218],"methods,":[58],"random":[59,148],"patch":[60,118],"selection":[61],"may":[62],"generate":[63,183],"meaningless":[64],"and":[67,86,136,176,188,206],"result":[68],"model":[70],"instability":[71],"during":[72],"process.":[75],"This":[76],"paper":[77],"proposes":[78],"strategy":[83],"termed":[84],"Semantic":[85],"Attentive":[87],"Mixing":[89],"(SADMix)":[90],"select":[92],"semantic":[93,158],"patches":[94,142,156],"for":[95,125],"generation":[97,127],"new":[99,129,184],"samples.":[101,131],"SADMix,":[103],"certain":[105],"number":[106],"critical":[108],"regions":[109,124],"are":[110,143,160,179,202],"localized":[111,123],"according":[112,145],"convolutional":[114],"activations.":[115],"An":[116],"image":[117,141,155],"selected":[120],"from":[121,150],"these":[122,140],"The":[132,192,210],"size,":[133],"aspect":[134],"ratio,":[135],"center":[137],"location":[138],"changed":[144],"values":[149],"beta":[152],"distribution.":[153],"These":[154],"with":[157],"information":[159],"used":[161],"mix":[163],"two":[164],"images.":[166],"According":[167],"class":[170],"activation":[171],"map":[172],"(CAM),":[173],"images":[175],"their":[177],"labels":[178],"mixed":[180,185],"proportionally":[181],"corresponding":[190],"labels.":[191],"SADMix":[194],"tested":[196],"on":[197],"three":[198],"fine-grained":[199],"datasets,":[200],"which":[201],"CUB-200-2011,":[203],"FGVC":[204],"Aircraft,":[205],"Stanford":[207],"Cars,":[208],"respectively.":[209],"experimental":[211],"results":[212],"confirm":[213],"effectiveness":[215],"SADMix.":[219]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-25T09:21:30.201066","created_date":"2025-10-10T00:00:00"}
