{"id":"https://openalex.org/W3009625749","doi":"https://doi.org/10.1109/ist48021.2019.9010104","title":"AGR-FCN: Adversarial Generated Region based on Fully Convolutional Networks for Single- and Multiple-Instance Object Detection","display_name":"AGR-FCN: Adversarial Generated Region based on Fully Convolutional Networks for Single- and Multiple-Instance Object Detection","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3009625749","doi":"https://doi.org/10.1109/ist48021.2019.9010104","mag":"3009625749"},"language":"en","primary_location":{"id":"doi:10.1109/ist48021.2019.9010104","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ist48021.2019.9010104","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","raw_type":"proceedings-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/A5100431257","display_name":"Rui Wang","orcid":"https://orcid.org/0000-0002-4792-1945"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Wang","raw_affiliation_strings":["Key Laboratory of Precision Optomechatronics Technology, Ministry of Education, School of Instrumentation Science and Opto-electronics Engineering, University of Beihang, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Precision Optomechatronics Technology, Ministry of Education, School of Instrumentation Science and Opto-electronics Engineering, University of Beihang, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081990773","display_name":"Runnan Qin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210115570","display_name":"National Space Science Center","ror":"https://ror.org/02nnjtm50","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210115570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Runnan Qin","raw_affiliation_strings":["National Space Science Center, Chinese Academy of Science, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Space Science Center, Chinese Academy of Science, Beijing, China","institution_ids":["https://openalex.org/I4210115570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070834436","display_name":"Jialing Zou","orcid":"https://orcid.org/0000-0002-5300-7279"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jialing Zou","raw_affiliation_strings":["Key Laboratory of Precision Optomechatronics Technology, Ministry of Education, School of Instrumentation Science and Opto-electronics Engineering, University of Beihang, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Precision Optomechatronics Technology, Ministry of Education, School of Instrumentation Science and Opto-electronics Engineering, University of Beihang, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100425271","display_name":"Liang Zhang","orcid":"https://orcid.org/0009-0001-7272-5540"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liang Zhang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Connecticut, 371 Fairfield Way, U-4157 Storrs, Connecticut, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Connecticut, 371 Fairfield Way, U-4157 Storrs, Connecticut, United States","institution_ids":["https://openalex.org/I140172145"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1017,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.48709591,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"0","issue":null,"first_page":"1","last_page":"6"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9937000274658203,"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":0.9886999726295471,"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":0.8001685738563538},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7731969356536865},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.6288444399833679},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5866128206253052},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.581406831741333},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5811633467674255},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5533973574638367},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.552017867565155},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5224456191062927},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5165411829948425},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41001832485198975}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8001685738563538},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7731969356536865},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6288444399833679},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5866128206253052},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.581406831741333},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5811633467674255},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5533973574638367},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.552017867565155},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5224456191062927},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5165411829948425},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41001832485198975},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ist48021.2019.9010104","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ist48021.2019.9010104","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W2037227137","https://openalex.org/W2099389217","https://openalex.org/W2102605133","https://openalex.org/W2125186487","https://openalex.org/W2407521645","https://openalex.org/W2793113313","https://openalex.org/W2894603674","https://openalex.org/W2951523806","https://openalex.org/W2962833723","https://openalex.org/W2963073614","https://openalex.org/W2963271314","https://openalex.org/W2963420272","https://openalex.org/W2963589288","https://openalex.org/W3106250896"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W4246396837","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W4310988119","https://openalex.org/W2969228573","https://openalex.org/W2963690996"],"abstract_inverted_index":{"Addressing":[0],"the":[1,24,27,59,88,92,104,111,139,143,148],"problem":[2],"that":[3,156],"object":[4,121],"instance":[5,131],"detection":[6,9,122,132,168],"has":[7],"poor":[8],"effect":[10],"on":[11,87,142],"occluded":[12,114],"objects":[13,115],"in":[14,32],"unstructured":[15],"environment":[16],"when":[17],"using":[18],"deep":[19,61],"learning":[20,31,44,97],"network,":[21],"we":[22],"explore":[23],"use":[25],"of":[26,29,95,106,113,170],"strategy":[28,94],"adversarial":[30,96],"this":[33],"paper.":[34],"A":[35],"three-step":[36],"pipeline":[37],"is":[38,84,124],"carried":[39],"to":[40,109,133],"build":[41],"a":[42],"novel":[43],"framework":[45],"denoted":[46],"as":[47,116,118],"Adversarial":[48,71],"Generated":[49],"Region-based":[50,63],"Fully":[51,64],"Convolutional":[52,65],"Networks":[53,66],"(AGR-FCN).":[54],"Our":[55],"method":[56],"first":[57],"training":[58,82,93],"noted":[60],"model":[62],"(R-FCN),":[67],"and":[68,101,147,163],"then":[69],"an":[70,166],"Mask":[72],"Dropout":[73],"Network":[74],"(AMDN),":[75],"which":[76,154],"can":[77,164],"generate":[78],"occlusion":[79],"features":[80,112],"for":[81,130],"samples,":[83],"designed":[85],"based":[86],"trained":[89],"R-FCN.":[90],"Through":[91],"between":[98],"network":[99,102,107],"R-FCN":[100,108,141,162],"AMDN,":[103],"ability":[105],"learn":[110],"well":[117],"its":[119],"instance-level":[120],"performance":[123],"improved.":[125],"Numerical":[126],"experiments":[127],"are":[128],"conducted":[129],"compare":[134],"our":[135,157],"proposed":[136,158],"AGR-FCN":[137,159],"with":[138],"original":[140,161],"self-made":[144],"BHGI":[145],"Database":[146],"public":[149],"database":[150],"GMU":[151],"Kitchen":[152],"Dataset,":[153],"demonstrate":[155],"outperforms":[160],"achieve":[165],"average":[167],"accuracy":[169],"nearly":[171],"90%.":[172]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
