{"id":"https://openalex.org/W4399659353","doi":"https://doi.org/10.1145/3641584.3641611","title":"Lightweight Image Classification Algorithm Based on Dual Knowledge Distillation","display_name":"Lightweight Image Classification Algorithm Based on Dual Knowledge Distillation","publication_year":2023,"publication_date":"2023-09-22","ids":{"openalex":"https://openalex.org/W4399659353","doi":"https://doi.org/10.1145/3641584.3641611"},"language":"en","primary_location":{"id":"doi:10.1145/3641584.3641611","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3641584.3641611","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 6th International Conference on Artificial Intelligence and Pattern Recognition (AIPR)","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/A5075379333","display_name":"Peng Xiao","orcid":"https://orcid.org/0009-0001-9966-0628"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Xiao","raw_affiliation_strings":["Center for Image and Information Processing,School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0009-0001-9966-0628","affiliations":[{"raw_affiliation_string":"Center for Image and Information Processing,School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100761763","display_name":"Ying Liu","orcid":"https://orcid.org/0000-0002-9037-7818"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Liu","raw_affiliation_strings":["Center for Image and Information Processing,School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0002-9037-7818","affiliations":[{"raw_affiliation_string":"Center for Image and Information Processing,School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089434715","display_name":"Yu Hao","orcid":"https://orcid.org/0000-0002-6820-5243"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Hao","raw_affiliation_strings":["Center for Image and Information Processing,School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0002-6820-5243","affiliations":[{"raw_affiliation_string":"Center for Image and Information Processing,School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210136859"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29871947,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"181","last_page":"187"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9937999844551086,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9937999844551086,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9907000064849854,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9871000051498413,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/distillation","display_name":"Distillation","score":0.707427978515625},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6778610348701477},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.658784806728363},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5049710869789124},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4673699736595154},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41627007722854614},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41471433639526367},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3426578938961029},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.0830511748790741},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.06996601819992065}],"concepts":[{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.707427978515625},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6778610348701477},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.658784806728363},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5049710869789124},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4673699736595154},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41627007722854614},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41471433639526367},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3426578938961029},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0830511748790741},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.06996601819992065},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3641584.3641611","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3641584.3641611","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 6th International Conference on Artificial Intelligence and Pattern Recognition (AIPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1676314349","https://openalex.org/W1981276685","https://openalex.org/W1985258161","https://openalex.org/W2112796928","https://openalex.org/W2122111042","https://openalex.org/W2161278885","https://openalex.org/W2165828254","https://openalex.org/W2618530766","https://openalex.org/W4231126656","https://openalex.org/W4293171766","https://openalex.org/W6608093658"],"related_works":["https://openalex.org/W3026162553","https://openalex.org/W2768175398","https://openalex.org/W2317351040","https://openalex.org/W2344382886","https://openalex.org/W19111321","https://openalex.org/W2412887479","https://openalex.org/W32245304","https://openalex.org/W2352553236","https://openalex.org/W2291489469","https://openalex.org/W2546503577"],"abstract_inverted_index":{"Deep":[0],"learning":[1,45],"models":[2,24],"perform":[3],"exceptionally":[4],"well":[5],"in":[6],"image":[7],"processing;":[8],"however,":[9],"the":[10,96,110,114,119,142,146,151,170],"increasing":[11],"number":[12],"of":[13],"parameters":[14],"and":[15,42,79,87,174],"computational":[16],"burdens":[17],"make":[18],"it":[19],"difficult":[20],"to":[21,40,94,105,113,117,123,137,145,149],"apply":[22],"these":[23],"on":[25,166,189],"mobile":[26],"devices.":[27],"To":[28],"address":[29],"this":[30],"issue,":[31],"a":[32,51,58,64,83,88],"widely":[33],"adopted":[34],"approach":[35],"is":[36,103,132,159],"using":[37],"knowledge":[38,48,108,140,163],"distillation":[39,49,78,101,130,164],"compress":[41],"optimize":[43],"deep":[44],"models.":[46],"Traditional":[47],"trains":[50],"student":[52,115,120,147,152],"network":[53,70,116,144,148],"with":[54,161],"soft":[55],"targets":[56],"from":[57,109,141],"teacher":[59,111,143],"network.":[60,98],"This":[61],"paper":[62],"proposes":[63],"Novel":[65],"Dual":[66],"Knowledge":[67],"Distillation":[68],"(NDKD)":[69],"by":[71,134],"integrating":[72],"Convolutional":[73],"Block":[74],"Attention":[75],"Module":[76],"(CBAM)":[77],"feature":[80,129],"distillation.":[81],"Specifically,":[82],"new":[84],"CBAM":[85,107],"module":[86,112],"hash":[89],"layer":[90],"are":[91],"first":[92],"added":[93],"improve":[95],"teacher-student":[97],"Next,":[99],"attention":[100],"loss":[102,131],"computed":[104],"transfer":[106,138],"enhance":[118,150],"network's":[121,153],"ability":[122],"capture":[124],"low-level":[125],"visual":[126],"features.":[127],"Finally,":[128],"constructed":[133],"hashing":[135],"layers":[136],"semantic":[139,155],"high-level":[154],"ability.":[156],"Our":[157],"method":[158,185],"compared":[160],"other":[162,187],"methods":[165,188],"benchmarking":[167],"datasets":[168],"including":[169],"CIFAR-10,":[171],"SVNM,":[172],"EMNIST,":[173],"Tire":[175],"Pattern":[176],"Image":[177],"dataset":[178],"CIIP-TPID.":[179],"Experimental":[180],"results":[181],"show":[182],"that":[183],"our":[184],"outperforms":[186],"adapted":[190],"datasets.":[191]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}