{"id":"https://openalex.org/W4387885916","doi":"https://doi.org/10.1109/tgrs.2023.3326992","title":"Discriminative Prototype Learning for Few-Shot Object Detection in Remote-Sensing Images","display_name":"Discriminative Prototype Learning for Few-Shot Object Detection in Remote-Sensing Images","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4387885916","doi":"https://doi.org/10.1109/tgrs.2023.3326992"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2023.3326992","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3326992","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},"type":"article","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/A5035306831","display_name":"M. Guo","orcid":"https://orcid.org/0009-0008-4302-5837"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Manke Guo","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0008-4302-5837","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008579555","display_name":"Yanan You","orcid":"https://orcid.org/0000-0001-6473-9187"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanan You","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6473-9187","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100453131","display_name":"Fang Liu","orcid":"https://orcid.org/0000-0003-4428-8758"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fang Liu","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4428-8758","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":2.0621,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.89078286,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"61","issue":null,"first_page":"1","last_page":"13"},"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":0.998199999332428,"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":0.998199999332428,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9966999888420105,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9921000003814697,"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":0.8520774841308594},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7464650869369507},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6132792830467224},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5926454067230225},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5799081325531006},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5773696899414062},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.533380389213562},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4597151279449463},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.38786581158638},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3788236975669861},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33206456899642944},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32772505283355713}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8520774841308594},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7464650869369507},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6132792830467224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5926454067230225},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5799081325531006},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5773696899414062},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.533380389213562},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4597151279449463},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.38786581158638},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3788236975669861},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33206456899642944},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32772505283355713},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2023.3326992","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3326992","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7599999904632568}],"awards":[{"id":"https://openalex.org/G6445369638","display_name":null,"funder_award_id":"62101060","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W2005368619","https://openalex.org/W2031489346","https://openalex.org/W2031614119","https://openalex.org/W2108598243","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2308318555","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2752782242","https://openalex.org/W2842511635","https://openalex.org/W2883725317","https://openalex.org/W2963150697","https://openalex.org/W2983156430","https://openalex.org/W2992240579","https://openalex.org/W2996583130","https://openalex.org/W3034771037","https://openalex.org/W3041133507","https://openalex.org/W3044832322","https://openalex.org/W3087124270","https://openalex.org/W3097651496","https://openalex.org/W3104135089","https://openalex.org/W3132437036","https://openalex.org/W3154360923","https://openalex.org/W3163623915","https://openalex.org/W3163832345","https://openalex.org/W3163842339","https://openalex.org/W3166058839","https://openalex.org/W3169708801","https://openalex.org/W3171153522","https://openalex.org/W3180725527","https://openalex.org/W3182874523","https://openalex.org/W3202403972","https://openalex.org/W3204910850","https://openalex.org/W3205363943","https://openalex.org/W3208208538","https://openalex.org/W3210092690","https://openalex.org/W4214666412","https://openalex.org/W4280563474","https://openalex.org/W4285220476","https://openalex.org/W4286632456","https://openalex.org/W4293584584","https://openalex.org/W4293811986","https://openalex.org/W4296106124","https://openalex.org/W4297808394","https://openalex.org/W4367680936","https://openalex.org/W6618372016","https://openalex.org/W6620707391","https://openalex.org/W6685380521","https://openalex.org/W6696085341","https://openalex.org/W6750227808","https://openalex.org/W6774983715"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W3208297503","https://openalex.org/W3119773509","https://openalex.org/W2889153461","https://openalex.org/W2964117661","https://openalex.org/W4388405611","https://openalex.org/W2619127353","https://openalex.org/W4309346246"],"abstract_inverted_index":{"Few-shot":[0],"object":[1],"detection":[2],"(FSOD)":[3],"in":[4,49,74,161],"remote":[5,118,163],"sensing":[6,119,164],"images,":[7],"which":[8],"aims":[9],"to":[10,27,87,104,129],"detect":[11],"never-seen":[12],"objects":[13],"with":[14,166],"few":[15],"training":[16,168],"samples,":[17,111],"has":[18],"attracted":[19],"wide":[20],"attention.":[21],"Some":[22],"recent":[23],"works":[24],"leverage":[25],"meta-learning":[26,160],"tackle":[28],"this":[29,54],"challenging":[30],"task":[31],"and":[32,42,76,97,149],"achieve":[33],"promising":[34],"performance.":[35],"However,":[36],"information":[37,90,108],"attenuation":[38],"during":[39],"feature":[40,72],"extraction":[41,73],"simple":[43],"prototype":[44],"representation":[45],"hamper":[46],"further":[47],"improvement":[48],"detecting":[50,162],"novel":[51,59],"classes.":[52],"In":[53],"article,":[55],"we":[56],"propose":[57],"a":[58,81,98,121],"meta-learning-based":[60,68],"FSOD":[61],"approach":[62],"named":[63],"DPL-Net.":[64],"Specially,":[65],"within":[66,91],"the":[67,106,115,131,157],"framework,":[69],"performing":[70],"role-specific":[71],"query":[75,92],"support":[77,110,135],"branches,":[78],"DPL-Net":[79],"adopts":[80],"Fine-grained":[82],"Information":[83,100],"Fusion":[84],"(FIF)":[85],"module":[86,103],"capture":[88],"scale-aware":[89],"regions":[93],"of":[94,109,117,134,159],"interest":[95],"(RoIs)":[96],"Multi-frequency":[99],"Enhancement":[101],"(MIE)":[102],"retain":[105],"spectral":[107],"respectively.":[112],"Moreover,":[113],"considering":[114],"variability":[116],"objects,":[120],"Discriminative":[122],"Prototype":[123],"Learning":[124],"(DPL)":[125],"strategy":[126],"is":[127],"developed":[128],"rectify":[130],"ambiguous":[132],"distribution":[133],"samples":[136],"for":[137],"more":[138],"representative":[139],"class-aware":[140],"prototypes.":[141],"Experiments":[142],"on":[143],"two":[144],"benchmark":[145],"datasets":[146],"(NWPU":[147],"VHR-10":[148],"DIOR)":[150],"demonstrate":[151],"that":[152],"our":[153],"method":[154],"effectively":[155],"improves":[156],"performance":[158],"images":[165],"limited":[167],"data.":[169]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}