{"id":"https://openalex.org/W4308236050","doi":"https://doi.org/10.1109/icip46576.2022.9897963","title":"Mask-Vit: an Object Mask Embedding in Vision Transformer for Fine-Grained Visual Classification","display_name":"Mask-Vit: an Object Mask Embedding in Vision Transformer for Fine-Grained Visual Classification","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4308236050","doi":"https://doi.org/10.1109/icip46576.2022.9897963"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9897963","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897963","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","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":"2022 IEEE International Conference on Image Processing (ICIP)","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/A5101854597","display_name":"Tong Su","orcid":"https://orcid.org/0000-0002-9752-2440"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]},{"id":"https://openalex.org/I4210155232","display_name":"Fiberhome Technology Group (China)","ror":"https://ror.org/04yv20134","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210155232"]}],"countries":["CN","HK"],"is_corresponding":false,"raw_author_name":"Tong Su","raw_affiliation_strings":["Chinese Academy of Sciences,Shenzhen Institute of Advanced Technology,Shenzhen,China","Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China","The Chinese University of Hong Kong, Hong Kong, China","Wuhan Research Institute of Post and Telecomunication, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Shenzhen Institute of Advanced Technology,Shenzhen,China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]},{"raw_affiliation_string":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]},{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]},{"raw_affiliation_string":"Wuhan Research Institute of Post and Telecomunication, Wuhan, China","institution_ids":["https://openalex.org/I4210155232"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069824702","display_name":"Shuo Ye","orcid":"https://orcid.org/0000-0001-7756-8233"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuo Ye","raw_affiliation_strings":["Huazhong University of Science and Technology,Wuhan,China","Huazhong University of Science and Technology, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology,Wuhan,China","institution_ids":["https://openalex.org/I47720641"]},{"raw_affiliation_string":"Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101537425","display_name":"Chengqun Song","orcid":"https://orcid.org/0000-0003-4723-5555"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN","HK"],"is_corresponding":false,"raw_author_name":"Chengqun Song","raw_affiliation_strings":["Chinese Academy of Sciences,Shenzhen Institute of Advanced Technology,Shenzhen,China","Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China","The Chinese University of Hong Kong, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Shenzhen Institute of Advanced Technology,Shenzhen,China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]},{"raw_affiliation_string":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]},{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101705358","display_name":"Jun Cheng","orcid":"https://orcid.org/0000-0002-3131-3275"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN","HK"],"is_corresponding":false,"raw_author_name":"Jun Cheng","raw_affiliation_strings":["Chinese Academy of Sciences,Shenzhen Institute of Advanced Technology,Shenzhen,China","Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China","The Chinese University of Hong Kong, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Shenzhen Institute of Advanced Technology,Shenzhen,China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]},{"raw_affiliation_string":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]},{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.177,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.51387804,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":"abs/1406.2952","issue":null,"first_page":"1626","last_page":"1630"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9987999796867371,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9983000159263611,"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":0.8231117725372314},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7199832797050476},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6779326796531677},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6111732721328735},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5287394523620605},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5108323097229004},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.47192811965942383},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.42195093631744385},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3996654152870178},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3835865259170532},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3779410421848297},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.17017453908920288}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8231117725372314},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7199832797050476},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6779326796531677},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6111732721328735},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5287394523620605},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5108323097229004},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.47192811965942383},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42195093631744385},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3996654152870178},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3835865259170532},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3779410421848297},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.17017453908920288},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9897963","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897963","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","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":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6499999761581421,"display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1616462885","https://openalex.org/W1955942245","https://openalex.org/W2104657103","https://openalex.org/W2462457117","https://openalex.org/W2737725206","https://openalex.org/W2780838211","https://openalex.org/W2797977484","https://openalex.org/W2889469641","https://openalex.org/W2896457183","https://openalex.org/W2963090248","https://openalex.org/W2963393555","https://openalex.org/W2963407932","https://openalex.org/W2964350391","https://openalex.org/W2969985801","https://openalex.org/W2986821660","https://openalex.org/W2998345525","https://openalex.org/W2998619563","https://openalex.org/W3034676907","https://openalex.org/W3035367622","https://openalex.org/W3081907075","https://openalex.org/W3096609285","https://openalex.org/W3139434170","https://openalex.org/W3206734547","https://openalex.org/W6636475194","https://openalex.org/W6637373629","https://openalex.org/W6638319203","https://openalex.org/W6739901393","https://openalex.org/W6755069125","https://openalex.org/W6755207826","https://openalex.org/W6784333009","https://openalex.org/W6788135285","https://openalex.org/W6792309431"],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W2037549926","https://openalex.org/W2345479200","https://openalex.org/W2183306018","https://openalex.org/W2849310602","https://openalex.org/W3006008237","https://openalex.org/W2419146053","https://openalex.org/W4388890789","https://openalex.org/W2088247287","https://openalex.org/W2565656575"],"abstract_inverted_index":{"Fine-grained":[0],"visual":[1],"classification":[2,49],"(FGVC)":[3],"targets":[4],"to":[5,91,130,135],"accurately":[6],"identify":[7],"the":[8,23,28,35,76,120,139,148,151],"subordinate":[9],"categories":[10],"from":[11],"a":[12,101],"target":[13],"class.":[14],"Convolutional":[15],"neural":[16],"network":[17,103],"(CNN)":[18],"based":[19],"methods":[20],"prove":[21],"that":[22,86],"attention":[24],"mechanism":[25,58],"can":[26,107],"enhance":[27],"representation":[29],"of":[30,54,150],"local":[31,89],"regions":[32],"and":[33,59,112,137],"improve":[34],"recognition":[36],"accuracy.":[37],"Recently,":[38],"vision":[39],"transformer":[40],"(ViT)":[41],"has":[42],"shown":[43],"great":[44],"application":[45],"potential":[46],"in":[47,75],"image":[48],"tasks":[50,85],"by":[51,117],"taking":[52],"advantage":[53],"its":[55],"inherent":[56],"self-attention":[57],"early":[60],"global":[61,67],"information":[62,68,134],"acquisition":[63,69],"capability.":[64],"However,":[65],"this":[66,97],"approach":[70],"involves":[71],"an":[72],"irrelevant":[73],"environment":[74],"interaction":[77],"process,":[78],"which":[79,106],"makes":[80],"it":[81],"difficult":[82],"for":[83],"fine-grained":[84],"rely":[87],"on":[88,119,144],"differences":[90],"quickly":[92],"learn":[93],"discriminant":[94],"features.":[95],"To":[96],"end,":[98],"we":[99],"propose":[100],"hybrid":[102],"termed":[104],"Mask-ViT,":[105],"effectively":[108],"avoid":[109],"environmental":[110],"interference":[111],"express":[113],"more":[114],"robust":[115],"features":[116],"focusing":[118],"instance":[121],"itself.":[122],"Specifically,":[123],"Contour":[124],"Knowledge":[125],"Embedding":[126],"(CKE)":[127],"is":[128],"employed":[129],"transferred":[131],"prior":[132],"location":[133],"ViT":[136],"guided":[138],"subsequent":[140],"recognition.":[141],"The":[142],"experiments":[143],"three":[145],"benchmarks":[146],"demonstrate":[147],"effectiveness":[149],"proposed":[152],"method.":[153]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}