{"id":"https://openalex.org/W4415744400","doi":"https://doi.org/10.1109/icivp66296.2025.00017","title":"CPRNet: Class Prototype Reweight Neural Network for Few-Shot Object Detection","display_name":"CPRNet: Class Prototype Reweight Neural Network for Few-Shot Object Detection","publication_year":2025,"publication_date":"2025-04-23","ids":{"openalex":"https://openalex.org/W4415744400","doi":"https://doi.org/10.1109/icivp66296.2025.00017"},"language":null,"primary_location":{"id":"doi:10.1109/icivp66296.2025.00017","doi":"https://doi.org/10.1109/icivp66296.2025.00017","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icivp66296.2025.00017","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Image and Video Processing (ICIVP)","raw_type":"proceedings-article"},"volume":null,"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/A5086316611","display_name":"Shan Su","orcid":"https://orcid.org/0009-0008-1055-7837","observed_orcids":["https://orcid.org/0009-0008-1055-7837","https://orcid.org/0009-0008-1113-932X"]},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuhong Shan","raw_affiliation_strings":["Harbin Engineering University,College of Computer Science and Technology,Harbin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Engineering University,College of Computer Science and Technology,Harbin,China","institution_ids":["https://openalex.org/I151727225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100327721","display_name":"Haoran Li","orcid":"https://orcid.org/0000-0002-2379-1541","observed_orcids":["https://orcid.org/0000-0002-2379-1541"]},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoran Li","raw_affiliation_strings":["Harbin Engineering University,College of Computer Science and Technology,Harbin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Engineering University,College of Computer Science and Technology,Harbin,China","institution_ids":["https://openalex.org/I151727225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060215503","display_name":"Yunfan Chen","orcid":"https://orcid.org/0000-0003-4808-6352","observed_orcids":["https://orcid.org/0000-0003-4808-6352","https://orcid.org/0000-0001-7248-5404"]},"institutions":[{"id":"https://openalex.org/I74525822","display_name":"Hubei University of Technology","ror":"https://ror.org/02d3fj342","country_code":"CN","type":"education","lineage":["https://openalex.org/I74525822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunfan Chen","raw_affiliation_strings":["Hubei University of Technology,Hubei Key Laboratory for High-Eficiency Utilization of Solar Energy and Operation Control of Energy Storage System,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hubei University of Technology,Hubei Key Laboratory for High-Eficiency Utilization of Solar Energy and Operation Control of Energy Storage System,Wuhan,China","institution_ids":["https://openalex.org/I74525822"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100574083","display_name":"Wenqi Zheng","orcid":"https://orcid.org/0009-0000-6028-1033","observed_orcids":["https://orcid.org/0009-0000-6028-1033"]},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenqi Zheng","raw_affiliation_strings":["Harbin Engineering University,College of Computer Science and Technology,Harbin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Engineering University,College of Computer Science and Technology,Harbin,China","institution_ids":["https://openalex.org/I151727225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29540041,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"52","last_page":"57"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9916999936103821,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9916999936103821,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.007899999618530273,"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":9.999999747378752e-05,"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/few-shot-object-detection","display_name":"few-shot object detection","score":1.0},{"id":"https://openalex.org/keywords/prototyping","display_name":"prototype development","score":0.4749999940395355},{"id":"https://openalex.org/keywords/spatial-attention","display_name":"spatial attention","score":0.6729999780654907},{"id":"https://openalex.org/keywords/adaptive-feature-enhancement","display_name":"adaptive feature enhancement","score":0.9670000076293945},{"id":"https://openalex.org/keywords/overfitting","display_name":"overfitting","score":0.6039999723434448},{"id":"https://openalex.org/keywords/support-set","display_name":"support set","score":0.45899999141693115},{"id":"https://openalex.org/keywords/query-sets","display_name":"query sets","score":0.6610000133514404},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"feature extraction","score":0.5329999923706055}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.9021999835968018},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8082000017166138},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.741599977016449},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7189000248908997},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6513000130653381},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6288999915122986},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.609000027179718},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5766000151634216},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.5486999750137329},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5442000031471252},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5256999731063843},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4641000032424927},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4359000027179718},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.43380001187324524},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.43059998750686646},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4072999954223633},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.374099999666214},{"id":"https://openalex.org/C182521987","wikidata":"https://www.wikidata.org/wiki/Q2493877","display_name":"Viola\u2013Jones object detection framework","level":5,"score":0.3450999855995178},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.3264000117778778},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30709999799728394},{"id":"https://openalex.org/C71681937","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object-class detection","level":5,"score":0.3057999908924103},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2978000044822693},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2703000009059906},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2623000144958496},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.2540000081062317}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icivp66296.2025.00017","doi":"https://doi.org/10.1109/icivp66296.2025.00017","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icivp66296.2025.00017","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Image and Video Processing (ICIVP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W2102605133","https://openalex.org/W2109255472","https://openalex.org/W2193145675","https://openalex.org/W2963150697","https://openalex.org/W2964241181","https://openalex.org/W3034974675","https://openalex.org/W3097651496","https://openalex.org/W3111679735","https://openalex.org/W3154360923","https://openalex.org/W3169708801","https://openalex.org/W3174017448","https://openalex.org/W3202110140","https://openalex.org/W4214522698","https://openalex.org/W4312696625","https://openalex.org/W4312702383","https://openalex.org/W4312755049","https://openalex.org/W4313192737","https://openalex.org/W4391648915","https://openalex.org/W4402030420"],"related_works":[],"abstract_inverted_index":{"Few-Shot":[0],"Object":[1],"Detection":[2],"(FSOD)":[3],"addresses":[4],"the":[5,30,38,41,51,101,119],"challenge":[6],"of":[7,22],"detecting":[8],"and":[9,61,112,131],"localizing":[10],"target":[11],"objects":[12],"in":[13,154],"a":[14,19,83,93,108,127,132],"test":[15],"set":[16,32,43],"when":[17],"only":[18],"limited":[20,74],"number":[21],"training":[23,75],"samples":[24,35],"are":[25],"available.":[26],"In":[27],"few-shot":[28,155],"learning,":[29],"support":[31],"provides":[33],"reference":[34],"to":[36,56,70,73],"guide":[37],"model,":[39],"while":[40],"query":[42],"evaluates":[44],"its":[45,152],"performance":[46,149],"on":[47,97,139],"unseen":[48],"data.":[49],"While":[50],"many":[52],"FSOD":[53],"models":[54],"struggle":[55],"fully":[57],"capture":[58],"contextual":[59],"information":[60],"fine-grained":[62],"details":[63],"during":[64],"feature":[65,105,115],"extraction,":[66],"which":[67],"can":[68],"lead":[69],"overfitting":[71],"due":[72],"diversity.":[76],"To":[77],"address":[78],"these":[79],"limitations,":[80],"we":[81],"propose":[82],"framework,":[84],"named":[85],"Class":[86],"Prototype":[87],"Reweight":[88],"Network":[89],"(CPRNet),":[90],"that":[91,143],"introduces":[92],"dual-strategy":[94],"improvement":[95],"based":[96],"DEVIT":[98],"module.":[99],"First,":[100],"FE-FSOD":[102],"module":[103,121],"enhances":[104],"extraction":[106],"through":[107],"spatial":[109],"attention":[110],"mechanism":[111],"an":[113],"adaptive":[114],"enhancement":[116],"algorithm.":[117,136],"Second,":[118],"PR-FSOD":[120],"refines":[122],"class":[123,133],"prototype":[124,134],"construction":[125],"using":[126],"dual-feature":[128],"aggregation":[129],"layer":[130],"optimization":[135],"Experimental":[137],"results":[138],"COCO":[140],"datasets":[141],"show":[142],"our":[144],"proposed":[145],"CPRNet":[146],"achieves":[147],"significant":[148],"gains,":[150],"demonstrating":[151],"effectiveness":[153],"object":[156],"detection":[157],"tasks.":[158]},"counts_by_year":[],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2025-10-31T00:00:00"}