{"id":"https://openalex.org/W4414170659","doi":"https://doi.org/10.1109/avss65446.2025.11149958","title":"Adaptive Attention Link-based Regularization for Vision Transformers","display_name":"Adaptive Attention Link-based Regularization for Vision Transformers","publication_year":2025,"publication_date":"2025-08-11","ids":{"openalex":"https://openalex.org/W4414170659","doi":"https://doi.org/10.1109/avss65446.2025.11149958"},"language":"en","primary_location":{"id":"doi:10.1109/avss65446.2025.11149958","is_oa":false,"landing_page_url":"https://doi.org/10.1109/avss65446.2025.11149958","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Advanced Visual and Signal-Based Systems (AVSS)","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/A5073623058","display_name":"Heegon Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Heegon Jin","raw_affiliation_strings":["Chung-Ang Univ.,Graduate School of Artificial Intelligence,Seoul,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chung-Ang Univ.,Graduate School of Artificial Intelligence,Seoul,Republic of Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086373443","display_name":"Jongwon Choi","orcid":"https://orcid.org/0000-0001-5757-9007"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jongwon Choi","raw_affiliation_strings":["Chung-Ang Univ.,Graduate School of Artificial Intelligence,Seoul,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chung-Ang Univ.,Graduate School of Artificial Intelligence,Seoul,Republic of Korea","institution_ids":["https://openalex.org/I67900169"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I67900169"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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":0.9987000226974487,"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":0.9987000226974487,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/transformer","display_name":"Transformer","score":0.6189000010490417},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5422999858856201},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5144000053405762},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4925999939441681},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4099000096321106},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.3765000104904175}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6897000074386597},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6189000010490417},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5861999988555908},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5422999858856201},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5144000053405762},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4925999939441681},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.427700012922287},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4099000096321106},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3765000104904175},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.3659000098705292},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3271999955177307},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.27790001034736633},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.27309998869895935},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.25760000944137573},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.25450000166893005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/avss65446.2025.11149958","is_oa":false,"landing_page_url":"https://doi.org/10.1109/avss65446.2025.11149958","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Advanced Visual and Signal-Based Systems (AVSS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320332195","display_name":"Samsung","ror":"https://ror.org/04w3jy968"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1821462560","https://openalex.org/W2064675550","https://openalex.org/W2108598243","https://openalex.org/W2533598788","https://openalex.org/W2924902521","https://openalex.org/W3008374555","https://openalex.org/W3033529678","https://openalex.org/W3034429256","https://openalex.org/W3105966348","https://openalex.org/W3118608800","https://openalex.org/W3121523901","https://openalex.org/W3137278571","https://openalex.org/W3138516171","https://openalex.org/W3159481202","https://openalex.org/W3177183540","https://openalex.org/W4214634256","https://openalex.org/W4214636423","https://openalex.org/W4287022992","https://openalex.org/W4287116734","https://openalex.org/W4287773438"],"related_works":[],"abstract_inverted_index":{"Transformer":[0],"networks":[1],"are":[2],"recently":[3],"employed":[4],"in":[5,48],"various":[6],"vision":[7],"tasks":[8],"with":[9,95,118],"outperforming":[10],"performance,":[11],"but":[12],"they":[13],"require":[14],"extensive":[15],"training":[16,21,44,51],"data":[17,54],"and":[18,53,73,108,159],"a":[19,25,67,119,147],"lengthy":[20],"time":[22,52],"to":[23,27,41],"train":[24],"model":[26],"disregard":[28],"an":[29,37],"inductive":[30],"bias.":[31],"In":[32],"this":[33],"paper,":[34],"we":[35,124,143],"present":[36],"adaptive":[38],"regularization":[39],"technique":[40,57],"improve":[42],"the":[43,62,74,84,100,113,127,132,151,165,169],"efficiency":[45],"of":[46,50,66,77,106,122,134,150,168],"ViT":[47,109,135,161],"terms":[49],"quantity.":[55],"Our":[56],"utilizes":[58],"trainable":[59],"links":[60,167],"between":[61,104,154],"channel-wise":[63],"spatial":[64],"attention":[65,75,85,89,102,115,162,170],"pre-trained":[68],"Convolution":[69],"Neural":[70],"Network":[71],"(CNN)":[72],"head":[76,163],"Vision":[78],"Transformers":[79],"(ViT),":[80],"which":[81],"is":[82,92],"called":[83],"augmentation":[86,90,171],"module.":[87,172],"The":[88],"module":[91],"trained":[93,114,166],"simultaneously":[94],"ViT,":[96],"so":[97],"automatically":[98],"discovers":[99],"complicated":[101],"similarity":[103],"levels":[105],"CNN":[107,156],"models.":[110],"By":[111],"considering":[112],"similarity,":[116],"even":[117],"small":[120],"amount":[121],"data,":[123],"validate":[125],"that":[126],"suggested":[128],"method":[129],"considerably":[130],"improves":[131],"performance":[133],"while":[136],"achieving":[137],"faster":[138],"convergence":[139],"during":[140],"training.":[141],"Additionally,":[142],"can":[144],"also":[145],"obtain":[146],"meaningful":[148],"analysis":[149],"relevant":[152],"relationship":[153],"each":[155,160],"activation":[157],"map":[158],"from":[164]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}