{"id":"https://openalex.org/W4405219170","doi":"https://doi.org/10.1109/tnnls.2024.3510391","title":"Multi-View Part-Based Few-Shot Object Detection","display_name":"Multi-View Part-Based Few-Shot Object Detection","publication_year":2024,"publication_date":"2024-12-10","ids":{"openalex":"https://openalex.org/W4405219170","doi":"https://doi.org/10.1109/tnnls.2024.3510391","pmid":"https://pubmed.ncbi.nlm.nih.gov/40030519"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2024.3510391","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2024.3510391","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5006664103","display_name":"Jingkai Ma","orcid":"https://orcid.org/0000-0002-8891-5146"},"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":true,"raw_author_name":"Jingkai Ma","raw_affiliation_strings":["School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0005-0739-8239","affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075698366","display_name":"Shuang Bai","orcid":"https://orcid.org/0000-0003-4586-8754"},"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":"Shuang Bai","raw_affiliation_strings":["School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4586-8754","affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5006664103"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.33554522,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"36","issue":"8","first_page":"14749","last_page":"14763"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9670000076293945,"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"}},"topics":[{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9670000076293945,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9616000056266785,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9596999883651733,"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/shot","display_name":"Shot (pellet)","score":0.6586135029792786},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5061864256858826},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4912058711051941},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4842964708805084},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4356829822063446},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.43520963191986084},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2264535129070282},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.12250679731369019}],"concepts":[{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.6586135029792786},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5061864256858826},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4912058711051941},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4842964708805084},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4356829822063446},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.43520963191986084},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2264535129070282},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.12250679731369019},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2024.3510391","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2024.3510391","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:40030519","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40030519","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2982477962","display_name":null,"funder_award_id":"2022JBMC003","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":75,"referenced_works":["https://openalex.org/W166750225","https://openalex.org/W639708223","https://openalex.org/W1496650988","https://openalex.org/W1968933322","https://openalex.org/W2059799772","https://openalex.org/W2145680191","https://openalex.org/W2158634074","https://openalex.org/W2165698076","https://openalex.org/W2168356304","https://openalex.org/W2479109623","https://openalex.org/W2565639579","https://openalex.org/W2590019597","https://openalex.org/W2765268259","https://openalex.org/W2773003563","https://openalex.org/W2799108620","https://openalex.org/W2963150697","https://openalex.org/W2964105864","https://openalex.org/W2964241181","https://openalex.org/W2964342398","https://openalex.org/W2983156430","https://openalex.org/W2996583130","https://openalex.org/W3018757597","https://openalex.org/W3034974675","https://openalex.org/W3035541121","https://openalex.org/W3044832322","https://openalex.org/W3103819522","https://openalex.org/W3106906018","https://openalex.org/W3107147550","https://openalex.org/W3108189450","https://openalex.org/W3110528336","https://openalex.org/W3126133447","https://openalex.org/W3162981726","https://openalex.org/W3164936045","https://openalex.org/W3166058839","https://openalex.org/W3166152027","https://openalex.org/W3169708801","https://openalex.org/W3174017448","https://openalex.org/W3174396556","https://openalex.org/W3180725527","https://openalex.org/W3180959016","https://openalex.org/W3191799143","https://openalex.org/W3200297852","https://openalex.org/W3201797941","https://openalex.org/W3202403972","https://openalex.org/W3210586215","https://openalex.org/W3211980987","https://openalex.org/W4212981014","https://openalex.org/W4214522698","https://openalex.org/W4214608919","https://openalex.org/W4214724741","https://openalex.org/W4289535682","https://openalex.org/W4301141993","https://openalex.org/W4312702383","https://openalex.org/W4312755049","https://openalex.org/W4319300005","https://openalex.org/W4320002685","https://openalex.org/W4366462713","https://openalex.org/W4379117118","https://openalex.org/W4382466690","https://openalex.org/W4385569616","https://openalex.org/W4386065738","https://openalex.org/W4386076362","https://openalex.org/W4388854781","https://openalex.org/W4390189905","https://openalex.org/W4392208006","https://openalex.org/W4392940036","https://openalex.org/W6636883489","https://openalex.org/W6717697761","https://openalex.org/W6771847183","https://openalex.org/W6774983715","https://openalex.org/W6790234625","https://openalex.org/W6803928784","https://openalex.org/W6842601527","https://openalex.org/W6848046888","https://openalex.org/W6929495744"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Few-shot":[0],"object":[1,8,24,26,114],"detection":[2,88],"(FSOD)":[3],"aims":[4],"to":[5,45,50,75,108,177],"detect":[6],"new":[7,90],"categories":[9],"with":[10,22,39],"a":[11,29,61,96,173],"few":[12,40],"annotated":[13],"samples":[14],"and":[15,106,129,164,179],"has":[16],"achieved":[17],"great":[18],"development.":[19],"However,":[20],"compared":[21],"general":[23],"detection,":[25],"misclassification":[27],"remains":[28],"serious":[30],"issue":[31],"in":[32,37,144,157,184],"FSOD.":[33],"This":[34],"is":[35],"because,":[36],"scenarios":[38],"samples,":[41],"the":[42,52,86,110,113,118,148,152],"model":[43],"struggles":[44],"learn":[46],"sufficient":[47],"discriminative":[48,70,78,141,155],"information":[49,71,142],"represent":[51],"corresponding":[53],"classes.":[54],"To":[55],"address":[56],"this":[57],"issue,":[58],"we":[59,93,120],"propose":[60,95],"multi-view":[62,125,132],"part-based":[63,97],"FSOD":[64],"network":[65],"(MPFSOD),":[66],"which":[67],"extracts":[68],"more":[69],"from":[72],"multiple":[73,145],"views":[74,146],"generate":[76],"highly":[77],"parts":[79,103,156],"that":[80,168],"effectively":[81],"characterize":[82],"objects,":[83],"thereby":[84],"promoting":[85],"accurate":[87],"of":[89,112,147,154],"categories.":[91],"Specifically,":[92],"first":[94],"detector":[98],"(PBD),":[99],"where":[100],"task-aware":[101],"object-level":[102],"are":[104],"generated":[105],"used":[107],"enhance":[109],"discriminativeness":[111],"representations.":[115],"Based":[116],"on":[117,161],"PBD,":[119],"then":[121],"introduce":[122],"an":[123,130],"image-level":[124],"fusion":[126],"module":[127,134],"(Img-MVF)":[128],"instance-level":[131],"modulation":[133],"(Inst-MVM).":[135],"These":[136],"two":[137],"modules":[138],"extract":[139],"richer":[140],"hidden":[143],"target,":[149],"further":[150],"facilitating":[151],"generation":[153],"PBD.":[158],"Extensive":[159],"experiments":[160],"PASCAL":[162],"VOC":[163],"MS":[165],"COCO":[166],"demonstrate":[167],"our":[169],"method":[170],"significantly":[171],"outperforms":[172],"strong":[174],"baseline":[175],"(up":[176],"11.2%)":[178],"previous":[180],"state-of-the-art":[181],"methods":[182],"(4.3%":[183],"average).":[185]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}