{"id":"https://openalex.org/W4285220476","doi":"https://doi.org/10.1109/lgrs.2022.3187566","title":"Few-Shot Object Detection of Remote Sensing Image via Calibration","display_name":"Few-Shot Object Detection of Remote Sensing Image via Calibration","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4285220476","doi":"https://doi.org/10.1109/lgrs.2022.3187566"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2022.3187566","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2022.3187566","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5008795478","display_name":"Ruolei Li","orcid":"https://orcid.org/0009-0008-9310-6600"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruolei Li","raw_affiliation_strings":["32182 Troops, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6774-2103","affiliations":[{"raw_affiliation_string":"32182 Troops, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049368183","display_name":"Yilong Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yilong Zeng","raw_affiliation_strings":["61932 Troops, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"61932 Troops, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101826689","display_name":"Jianfeng Wu","orcid":"https://orcid.org/0000-0003-3990-5897"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jianfeng Wu","raw_affiliation_strings":["32182 Troops, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"32182 Troops, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100633726","display_name":"Yongli Wang","orcid":"https://orcid.org/0000-0001-8221-451X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yongli Wang","raw_affiliation_strings":["32182 Troops, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"32182 Troops, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100404549","display_name":"Xiaoli Zhang","orcid":"https://orcid.org/0000-0001-8412-4956"},"institutions":[{"id":"https://openalex.org/I4210167189","display_name":"Academy of Military Transportation","ror":"https://ror.org/05nm40v04","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210167189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoli Zhang","raw_affiliation_strings":["Basic Research Department, Army Military Transportation University of PLA, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Basic Research Department, Army Military Transportation University of PLA, Tianjin, China","institution_ids":["https://openalex.org/I4210167189"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8809,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.73850744,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.998199999332428,"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":0.998199999332428,"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":0.9976999759674072,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9970999956130981,"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":0.8198224902153015},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6996303200721741},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.6056658029556274},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.5756450891494751},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5671976804733276},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.49796462059020996},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.48987099528312683},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4767266809940338},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46437421441078186},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.43027687072753906},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.41462963819503784},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.40470871329307556},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10793286561965942}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8198224902153015},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6996303200721741},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.6056658029556274},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5756450891494751},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5671976804733276},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.49796462059020996},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.48987099528312683},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4767266809940338},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46437421441078186},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.43027687072753906},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.41462963819503784},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.40470871329307556},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10793286561965942},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2022.3187566","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2022.3187566","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2005368619","https://openalex.org/W2194775991","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2601450892","https://openalex.org/W2983156430","https://openalex.org/W2992240579","https://openalex.org/W3035654071","https://openalex.org/W3132437036","https://openalex.org/W3176733230","https://openalex.org/W3205249428","https://openalex.org/W3205363943","https://openalex.org/W4214724741","https://openalex.org/W4293584584","https://openalex.org/W4312894362","https://openalex.org/W6735236233","https://openalex.org/W6750227808","https://openalex.org/W6810994575"],"related_works":["https://openalex.org/W4327500857","https://openalex.org/W4311223090","https://openalex.org/W1689909837","https://openalex.org/W2965994363","https://openalex.org/W4205729548","https://openalex.org/W1895541646","https://openalex.org/W4301521271","https://openalex.org/W2889698616","https://openalex.org/W2953362004","https://openalex.org/W4298525700"],"abstract_inverted_index":{"Few-shot":[0],"object":[1],"detection":[2],"(FSOD),":[3],"which":[4,58],"aims":[5],"at":[6],"detecting":[7],"rare":[8],"objects":[9],"based":[10],"on":[11,156],"few":[12],"training":[13,64],"samples":[14],"has":[15],"attracted":[16],"significant":[17],"research":[18],"interest.":[19],"Previous":[20],"approaches":[21],"find":[22],"that":[23],"the":[24,60,70,73,83,87,105,112,121,133,140,164,178],"performance":[25],"degradation":[26],"of":[27,62,86,166],"FSOD":[28],"is":[29,79,143],"mainly":[30],"caused":[31],"by":[32,95,115,126,145],"category":[33,135,141],"confusion":[34],"(high":[35],"false":[36],"positives).":[37],"To":[38],"solve":[39],"this":[40],"issue,":[41],"we":[42],"propose":[43],"a":[44,96,116,127,146],"two-stage":[45],"fine-tuning":[46,67],"approach":[47],"via":[48],"classification":[49,148],"score":[50,142,149],"calibration":[51,150],"for":[52,132,170],"remote":[53],"sensing":[54],"images,":[55],"named":[56],"TFACSC,":[57],"follows":[59],"flowchart":[61],"base":[63],"and":[65,136,139,160,180],"few-shot":[66],"to":[68,81,110],"train":[69],"detector.":[71],"First,":[72],"backbone":[74],"with":[75],"strong":[76],"representation":[77],"ability":[78],"employed":[80],"extract":[82],"multi-scale":[84],"features":[85,92,107],"query":[88],"image.":[89],"Then,":[90],"these":[91],"are":[93,108,124],"aggregated":[94,106],"novel":[97,147],"multi-head":[98],"scaled":[99],"cosine":[100],"non-local":[101],"module":[102,151],"(MSCN).":[103],"Next,":[104],"used":[109],"generate":[111],"objectiveness":[113],"proposals":[114,123],"region":[117],"proposal":[118],"network.":[119],"Eventually,":[120],"generated":[122],"refined":[125],"bounding":[128],"box":[129],"prediction":[130],"head":[131],"final":[134],"position":[137],"prediction,":[138],"calibrated":[144],"(CSCM).":[152],"Extensive":[153],"experiments":[154],"conducted":[155],"NWPU":[157],"VHR":[158],"10":[159],"DIOR":[161],"benchmarks":[162],"demonstrate":[163],"effectiveness":[165],"our":[167,174],"model.":[168],"Particularly,":[169],"any":[171],"shot":[172],"cases,":[173],"method":[175],"greatly":[176],"outperforms":[177],"baseline":[179],"achieves":[181],"state-of-the-art":[182],"performance.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
