{"id":"https://openalex.org/W4407467326","doi":"https://doi.org/10.1109/iccvit63928.2024.10872480","title":"Evaluation of Deep Learning Methods for Age-appropriate Sensitive Image Classification","display_name":"Evaluation of Deep Learning Methods for Age-appropriate Sensitive Image Classification","publication_year":2024,"publication_date":"2024-11-24","ids":{"openalex":"https://openalex.org/W4407467326","doi":"https://doi.org/10.1109/iccvit63928.2024.10872480"},"language":"en","primary_location":{"id":"doi:10.1109/iccvit63928.2024.10872480","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvit63928.2024.10872480","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 2nd International Conference on Computer, Vision and Intelligent Technology (ICCVIT)","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/A5100407437","display_name":"Dong Liu","orcid":"https://orcid.org/0000-0002-4095-7531"},"institutions":[{"id":"https://openalex.org/I43081956","display_name":"Xiangnan University","ror":"https://ror.org/05by9mg64","country_code":"CN","type":"education","lineage":["https://openalex.org/I43081956"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Liu","raw_affiliation_strings":["Xiangnan University,School of Computer and Artificial Intelligence,Chenzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiangnan University,School of Computer and Artificial Intelligence,Chenzhou,China","institution_ids":["https://openalex.org/I43081956"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100401289","display_name":"Feng Zhang","orcid":"https://orcid.org/0000-0002-8809-6910"},"institutions":[{"id":"https://openalex.org/I43081956","display_name":"Xiangnan University","ror":"https://ror.org/05by9mg64","country_code":"CN","type":"education","lineage":["https://openalex.org/I43081956"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Zhang","raw_affiliation_strings":["Xiangnan University,School of Computer and Artificial Intelligence,Chenzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiangnan University,School of Computer and Artificial Intelligence,Chenzhou,China","institution_ids":["https://openalex.org/I43081956"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014708963","display_name":"Tinghui Ouyang","orcid":"https://orcid.org/0000-0002-2752-9700"},"institutions":[{"id":"https://openalex.org/I43081956","display_name":"Xiangnan University","ror":"https://ror.org/05by9mg64","country_code":"CN","type":"education","lineage":["https://openalex.org/I43081956"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingyu Ouyang","raw_affiliation_strings":["Xiangnan University,School of Computer and Artificial Intelligence,Chenzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiangnan University,School of Computer and Artificial Intelligence,Chenzhou,China","institution_ids":["https://openalex.org/I43081956"]}]},{"author_position":"last","author":{"id":null,"display_name":"Caineng Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I43081956","display_name":"Xiangnan University","ror":"https://ror.org/05by9mg64","country_code":"CN","type":"education","lineage":["https://openalex.org/I43081956"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Caineng Zhang","raw_affiliation_strings":["Xiangnan University,School of Computer and Artificial Intelligence,Chenzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiangnan University,School of Computer and Artificial Intelligence,Chenzhou,China","institution_ids":["https://openalex.org/I43081956"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I43081956"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.33956921,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":0.9020000100135803,"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":0.9020000100135803,"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":0.6647375822067261},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6371983289718628},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.6104910969734192},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5986050367355347},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.46863555908203125},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4094696044921875},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40687787532806396},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38182154297828674}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6647375822067261},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6371983289718628},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.6104910969734192},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5986050367355347},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.46863555908203125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4094696044921875},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40687787532806396},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38182154297828674}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccvit63928.2024.10872480","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvit63928.2024.10872480","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 2nd International Conference on Computer, Vision and Intelligent Technology (ICCVIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W1996643156","https://openalex.org/W2167912153","https://openalex.org/W2784418822","https://openalex.org/W2963163009","https://openalex.org/W3138516171"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W2731899572","https://openalex.org/W4230611425","https://openalex.org/W4294635752","https://openalex.org/W4304166257","https://openalex.org/W4383066092","https://openalex.org/W4380075502","https://openalex.org/W2795259429","https://openalex.org/W2546503577"],"abstract_inverted_index":{"Currently,":[0],"communication":[1],"media":[2,18,25,91],"such":[3],"as":[4,167],"movies":[5],"have":[6],"a":[7,39,132,137],"more":[8,34],"mature":[9],"grading":[10,55,77],"system,":[11],"but":[12],"images":[13,31,58,79],"widely":[14],"disseminated":[15],"on":[16,66,89,130,154,177],"social":[17,90,178],"do":[19],"not.":[20],"Additionally,":[21],"in":[22,116,163],"the":[23,27,43,50,96,155],"new":[24],"environment,":[26],"dissemination":[28,88,174],"of":[29,46,52,98,118],"sensitive":[30,57,78,123],"has":[32],"become":[33],"covert":[35],"and":[36,112,136,147,175],"diversified,":[37],"posing":[38],"potential":[40],"threat":[41],"to":[42,83,121],"mental":[44],"health":[45],"adolescents.":[47],"Therefore,":[48],"solving":[49],"challenge":[51],"implementing":[53],"age-appropriate":[54,74,139],"for":[56,76,86,171],"using":[59],"computer":[60],"vision":[61],"technology":[62],"is":[63],"crucial.":[64],"Based":[65],"this":[67,69,164],"premise,":[68],"paper":[70,165],"first":[71],"constructs":[72],"an":[73,168],"dataset":[75,135],"targeted":[80],"at":[81],"teenagers":[82],"provide":[84],"reference":[85,170],"image":[87,173],"platforms.":[92],"Then,":[93],"it":[94],"evaluates":[95],"performance":[97,153],"eight":[99],"deep":[100],"learning":[101],"models":[102,115],"including":[103],"ResNet101,":[104],"ShuffleNet-v1,":[105],"Vgg11,":[106],"Vgg16,":[107],"DenseNet101,":[108],"MobileNet-v2,":[109],"Swin":[110,148],"Transformer,":[111],"Vision":[113],"Transformer":[114,149],"terms":[117],"their":[119],"ability":[120],"recognize":[122],"images.":[124],"We":[125],"finally":[126],"conduct":[127],"comprehensive":[128],"tests":[129],"both":[131],"2-category":[133],"public":[134],"quaternary":[138],"dataset;":[140],"experimental":[141],"results":[142],"demonstrate":[143],"that":[144],"MobileNet-v2":[145],"network":[146,150],"exhibit":[151],"superior":[152],"two":[156],"datasets":[157],"respectively.":[158],"The":[159],"research":[160],"findings":[161],"presented":[162],"serve":[166],"important":[169],"managing":[172],"classification":[176],"media.":[179]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}