{"id":"https://openalex.org/W4416249953","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228454","title":"A Novel Forgetting Mitigation Approach for YOLOv7-Enabled Incremental Object Detection","display_name":"A Novel Forgetting Mitigation Approach for YOLOv7-Enabled Incremental Object Detection","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416249953","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228454"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228454","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228454","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5100434916","display_name":"Qing Wang","orcid":"https://orcid.org/0009-0004-0545-755X"},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qing Wang","raw_affiliation_strings":["Shenyang Aerospace University,Shenyang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100453075","display_name":"Fang Liu","orcid":"https://orcid.org/0000-0002-5669-9354"},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fang Liu","raw_affiliation_strings":["Shenyang Aerospace University,Shenyang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jiawei Yue","orcid":null},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawei Yue","raw_affiliation_strings":["Shenyang Aerospace University,Shenyang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102497597","display_name":"Qin Dai","orcid":"https://orcid.org/0009-0003-6355-5943"},"institutions":[{"id":"https://openalex.org/I108904480","display_name":"Shenyang Institute of Engineering","ror":"https://ror.org/02pfsj857","country_code":"CN","type":"education","lineage":["https://openalex.org/I108904480"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qin Dai","raw_affiliation_strings":["Shenyang Institute of Engineering,Shenyang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang Institute of Engineering,Shenyang,China","institution_ids":["https://openalex.org/I108904480"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101798674","display_name":"Xiangbin Shi","orcid":"https://orcid.org/0000-0001-7028-3649"},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangbin Shi","raw_affiliation_strings":["Shenyang Aerospace University,Shenyang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6514000296592712,"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":0.6514000296592712,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.1298999935388565,"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":0.12759999930858612,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/forgetting","display_name":"Forgetting","score":0.7002999782562256},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6434000134468079},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.616100013256073},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5921000242233276},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.46959999203681946},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.4359999895095825},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.42410001158714294},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4147999882698059},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4099000096321106}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7089999914169312},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7041000127792358},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.7002999782562256},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6434000134468079},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.616100013256073},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5921000242233276},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.46959999203681946},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4611999988555908},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.4359999895095825},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.42410001158714294},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4147999882698059},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4099000096321106},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4097999930381775},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4016000032424927},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3718000054359436},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3555999994277954},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.33500000834465027},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.33390000462532043},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.32179999351501465},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3215000033378601},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.2921000123023987},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.26589998602867126},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2583000063896179},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228454","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228454","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":14,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2145339207","https://openalex.org/W2193145675","https://openalex.org/W2630837129","https://openalex.org/W2962966271","https://openalex.org/W2966730026","https://openalex.org/W3007041883","https://openalex.org/W3021931813","https://openalex.org/W3096609285","https://openalex.org/W3167554914","https://openalex.org/W4226029051","https://openalex.org/W4313141028","https://openalex.org/W4386076281","https://openalex.org/W4386076325"],"related_works":[],"abstract_inverted_index":{"Incremental":[0],"Object":[1],"Detection":[2],"(IOD)":[3],"is":[4,86,136],"hampered":[5],"by":[6],"catastrophic":[7],"forgetting":[8],"from":[9,112],"continuous":[10],"learning":[11],"as":[12],"object":[13],"categories":[14],"evolve,":[15],"particularly":[16],"in":[17,62,102,142],"real-world":[18],"tasks.":[19],"This":[20],"paper":[21],"focuses":[22],"on":[23,155],"tackling":[24],"IOD":[25],"challenges":[26],"and":[27,104,121],"adopting":[28],"the":[29,67,75,90,95,105,113,127,140,147],"lightweight":[30],"You":[31],"Only":[32],"Look":[33],"Once":[34],"(YOLO)":[35],"detector":[36],"suitable":[37],"for":[38,52],"prevalent":[39],"edge":[40],"devices.":[41],"To":[42],"preserve":[43],"prior":[44],"knowledge,":[45],"we":[46],"propose":[47],"a":[48,80,119,132],"training":[49],"freezing":[50],"strategy":[51],"network":[53],"layers":[54],"that":[55,159],"extract":[56],"common":[57],"features":[58],"of":[59,149],"all":[60],"classes":[61],"new":[63],"category":[64,143],"learning,":[65],"employing":[66],"Deep":[68],"Deterministic":[69],"Policy":[70],"Gradient":[71],"(DDPG)":[72],"to":[73,93,108,125,139,145],"guide":[74],"layer":[76],"chosen":[77],"process.":[78],"Additionally,":[79],"novel":[81],"Knowledge":[82],"Distillation":[83],"(KD)":[84],"approach":[85],"presented.":[87],"It":[88],"introduces":[89],"focal":[91],"global":[92],"address":[94],"discrepancy":[96],"between":[97],"teacher-student":[98],"feature":[99],"maps":[100],"inherent":[101],"KD":[103],"response":[106],"distillation":[107,128],"offer":[109],"reasoning":[110],"information":[111],"old":[114],"detector.":[115],"We":[116],"also":[117],"build":[118],"spatial":[120],"channel":[122],"attention":[123],"module":[124],"enhance":[126],"performance":[129],"further.":[130],"Moreover,":[131],"Bi-fold":[133],"sampling":[134],"method":[135,161],"developed":[137],"according":[138],"balance":[141],"probability":[144],"keep":[146],"capability":[148],"picking":[150],"representative":[151],"exemplars":[152],"continuous.":[153],"Experiments":[154],"MS":[156],"COCO":[157],"show":[158],"our":[160],"gets":[162],"State-Of-The-Art":[163],"results.":[164]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-14T00:00:00"}