{"id":"https://openalex.org/W4386090261","doi":"https://doi.org/10.1109/access.2023.3307490","title":"DA-FSOD: A Novel Data Augmentation Scheme for Few-Shot Object Detection","display_name":"DA-FSOD: A Novel Data Augmentation Scheme for Few-Shot Object Detection","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4386090261","doi":"https://doi.org/10.1109/access.2023.3307490"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3307490","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3307490","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10227279.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10227279.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028274897","display_name":"Jian Wei Yao","orcid":"https://orcid.org/0000-0002-9403-4572"},"institutions":[{"id":"https://openalex.org/I4210141966","display_name":"China Academy of Railway Sciences","ror":"https://ror.org/051wv2j09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210141966"]},{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jian Yao","raw_affiliation_strings":["Postgraduate Department, China Academy of Railway Sciences, Beijing, China","School of Information Management, Beijing Information Science and Technology University, Beijing, China","China Academy of Railway Sciences Corporation Limited, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Postgraduate Department, China Academy of Railway Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210141966"]},{"raw_affiliation_string":"School of Information Management, Beijing Information Science and Technology University, Beijing, China","institution_ids":["https://openalex.org/I78675632"]},{"raw_affiliation_string":"China Academy of Railway Sciences Corporation Limited, Beijing, China","institution_ids":["https://openalex.org/I4210141966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019391220","display_name":"Tianyun Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210141966","display_name":"China Academy of Railway Sciences","ror":"https://ror.org/051wv2j09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210141966"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianyun Shi","raw_affiliation_strings":["China Academy of Railway Sciences Corporation Ltd., Beijing, China","China Academy of Railway Sciences Corporation Limited, Beijing, China"],"affiliations":[{"raw_affiliation_string":"China Academy of Railway Sciences Corporation Ltd., Beijing, China","institution_ids":["https://openalex.org/I4210141966"]},{"raw_affiliation_string":"China Academy of Railway Sciences Corporation Limited, Beijing, China","institution_ids":["https://openalex.org/I4210141966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063069550","display_name":"Xiaoping Che","orcid":"https://orcid.org/0000-0002-5651-6909"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoping Che","raw_affiliation_strings":["School of Software Engineering, Beijing Jiaotong University, Beijing, China","School of software engineering, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Software Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"School of software engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101846340","display_name":"Jie Yao","orcid":"https://orcid.org/0000-0003-0533-6657"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]},{"id":"https://openalex.org/I4210141966","display_name":"China Academy of Railway Sciences","ror":"https://ror.org/051wv2j09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210141966"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Yao","raw_affiliation_strings":["Postgraduate Department, China Academy of Railway Sciences, Beijing, China","School of Information Management, Beijing Information Science and Technology University, Beijing, China","The School of Information Management, Beijing Information Science and Technology University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Postgraduate Department, China Academy of Railway Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210141966"]},{"raw_affiliation_string":"School of Information Management, Beijing Information Science and Technology University, Beijing, China","institution_ids":["https://openalex.org/I78675632"]},{"raw_affiliation_string":"The School of Information Management, Beijing Information Science and Technology University, Beijing, China","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000364181","display_name":"Liuyi Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210141966","display_name":"China Academy of Railway Sciences","ror":"https://ror.org/051wv2j09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210141966"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liuyi Wu","raw_affiliation_strings":["China Academy of Railway Sciences Corporation Ltd., Beijing, China","China Academy of Railway Sciences Corporation Limited, Beijing, China"],"affiliations":[{"raw_affiliation_string":"China Academy of Railway Sciences Corporation Ltd., Beijing, China","institution_ids":["https://openalex.org/I4210141966"]},{"raw_affiliation_string":"China Academy of Railway Sciences Corporation Limited, Beijing, China","institution_ids":["https://openalex.org/I4210141966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5028274897"],"corresponding_institution_ids":["https://openalex.org/I4210141966","https://openalex.org/I78675632"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.8599,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.7550501,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"11","issue":null,"first_page":"92100","last_page":"92110"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9997000098228455,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9972000122070312,"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":0.8564543724060059},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7633343935012817},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6192405819892883},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5286118388175964},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.520809531211853},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48136788606643677},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.44496774673461914},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.419749915599823},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4158335328102112},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41113072633743286},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40438181161880493},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3658459782600403},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34883707761764526}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8564543724060059},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7633343935012817},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6192405819892883},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5286118388175964},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.520809531211853},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48136788606643677},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.44496774673461914},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.419749915599823},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4158335328102112},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41113072633743286},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40438181161880493},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3658459782600403},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34883707761764526},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3307490","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3307490","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10227279.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f3e17a4c20f54beb9fa295a74c0a0a0b","is_oa":true,"landing_page_url":"https://doaj.org/article/f3e17a4c20f54beb9fa295a74c0a0a0b","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 92100-92110 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3307490","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3307490","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10227279.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G123214604","display_name":null,"funder_award_id":"2020QNRC001","funder_id":"https://openalex.org/F4320330170","funder_display_name":"Young Elite Scientists Sponsorship Program by Tianjin"}],"funders":[{"id":"https://openalex.org/F4320330170","display_name":"Young Elite Scientists Sponsorship Program by Tianjin","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386090261.pdf","grobid_xml":"https://content.openalex.org/works/W4386090261.grobid-xml"},"referenced_works_count":53,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1673923490","https://openalex.org/W2031489346","https://openalex.org/W2193145675","https://openalex.org/W2565639579","https://openalex.org/W2746314669","https://openalex.org/W2765407302","https://openalex.org/W2788210750","https://openalex.org/W2936774411","https://openalex.org/W2952742172","https://openalex.org/W2954996726","https://openalex.org/W2962837320","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W2963351448","https://openalex.org/W2963870144","https://openalex.org/W2964105864","https://openalex.org/W2971296908","https://openalex.org/W2983156430","https://openalex.org/W2988803701","https://openalex.org/W2992308087","https://openalex.org/W2994088087","https://openalex.org/W2995351650","https://openalex.org/W2996583130","https://openalex.org/W3009213340","https://openalex.org/W3018757597","https://openalex.org/W3034858314","https://openalex.org/W3035682985","https://openalex.org/W3049752343","https://openalex.org/W3106250896","https://openalex.org/W3110528336","https://openalex.org/W3126121388","https://openalex.org/W3162566909","https://openalex.org/W3169708801","https://openalex.org/W3173777717","https://openalex.org/W3174828871","https://openalex.org/W3207208339","https://openalex.org/W4292062030","https://openalex.org/W4295727797","https://openalex.org/W4312295406","https://openalex.org/W6620707391","https://openalex.org/W6629606793","https://openalex.org/W6637162671","https://openalex.org/W6743428213","https://openalex.org/W6745136726","https://openalex.org/W6751795773","https://openalex.org/W6770145604","https://openalex.org/W6770979763","https://openalex.org/W6774983715","https://openalex.org/W6777046832","https://openalex.org/W6780927959","https://openalex.org/W6790003725"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W1975289146","https://openalex.org/W2611989081","https://openalex.org/W2105887828","https://openalex.org/W2949096641","https://openalex.org/W2970686063","https://openalex.org/W4320729701","https://openalex.org/W4254103348","https://openalex.org/W3210378990","https://openalex.org/W3034745255"],"abstract_inverted_index":{"Deep":[0],"learning":[1,33],"techniques":[2,37],"continue":[3],"to":[4,18,50,69,80,121,189],"be":[5],"used":[6],"in":[7,10,142],"various":[8],"applications":[9],"recent":[11],"years.":[12],"However,":[13],"when":[14],"it":[15],"is":[16,47],"difficult":[17],"obtain":[19],"adequate":[20],"training":[21,75,152],"samples,":[22],"the":[23,26,42,71,108,116,130,136,166,177],"performance":[24],"of":[25,44,73,118,139,158,179],"depth":[27],"model":[28,74,143,167],"will":[29],"degrade.":[30],"Although":[31],"few-shot":[32,180],"and":[34,110,128,144],"data":[35,46,82,88,105,148],"enhancement":[36],"can":[38],"relieve":[39],"this":[40,54],"dilemma,":[41],"diversity":[43],"real":[45],"too":[48],"large":[49],"simulate.":[51],"To":[52,125],"tackle":[53],"challenge,":[55],"we":[56,85,101,134],"study":[57],"a":[58,87,103],"novel":[59,104],"method,":[60],"Data":[61],"Augmentation":[62],"Scheme":[63],"For":[64],"Few-Shot":[65],"Object":[66],"Detection":[67],"(DA-FSOD),":[68],"improve":[70],"efficiency":[72],"on":[76,94,176],"visual":[77],"tasks.":[78],"Specifically,":[79],"expand":[81],"augmentation":[83,89,106,149],"space,":[84],"build":[86],"operation":[90],"pool":[91],"(DAOP)":[92],"based":[93],"several":[95],"common-applied":[96],"image":[97,141],"process":[98],"operations.":[99],"Then":[100],"propose":[102,145],"scheme,":[107,113],"series":[109],"parallel":[111],"connection":[112],"which":[114,150],"superimposes":[115],"effects":[117],"different":[119],"operations":[120],"generate":[122],"diverse":[123],"variants.":[124,160],"further":[126],"explore":[127],"utilize":[129],"deep":[131,156],"feature":[132],"information,":[133],"leverage":[135],"semantic":[137,147],"information":[138],"input":[140],"imposed":[146],"augments":[151],"set":[153],"semantically":[154],"via":[155],"features":[157],"augmented":[159],"The":[161,183],"proposed":[162],"method":[163],"successfully":[164],"enhanced":[165],"performance.":[168],"We":[169],"validated":[170],"our":[171],"approach":[172],"using":[173],"extensive":[174],"experiments":[175],"domain":[178],"object":[181],"detection.":[182],"results":[184],"showed":[185],"remarkable":[186],"gains":[187],"compared":[188],"state-of-the-art":[190],"methods.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1}],"updated_date":"2026-02-25T23:00:34.991745","created_date":"2025-10-10T00:00:00"}