{"id":"https://openalex.org/W4283276033","doi":"https://doi.org/10.1145/3529836.3529854","title":"Faster R-CNN with Generative Adversarial Occlusion Network for Object Detection","display_name":"Faster R-CNN with Generative Adversarial Occlusion Network for Object Detection","publication_year":2022,"publication_date":"2022-02-18","ids":{"openalex":"https://openalex.org/W4283276033","doi":"https://doi.org/10.1145/3529836.3529854"},"language":"en","primary_location":{"id":"doi:10.1145/3529836.3529854","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3529836.3529854","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3529836.3529854","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3529836.3529854","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036356066","display_name":"Fenglie Li","orcid":null},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fenglie Li","raw_affiliation_strings":["Washington University in St. Louis, Washington University in St. Louis, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Washington University in St. Louis, Washington University in St. Louis, USA","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101476431","display_name":"Li Jiehui","orcid":"https://orcid.org/0009-0001-2719-3151"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiehui Li","raw_affiliation_strings":["Ohio State University, Ohio State University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ohio State University, Ohio State University, USA","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014828561","display_name":"Yancong Deng","orcid":"https://orcid.org/0000-0001-7047-1164"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yancong Deng","raw_affiliation_strings":["University of California San Diego, University of California San Diego, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California San Diego, University of California San Diego, USA","institution_ids":["https://openalex.org/I36258959"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1015,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.36752511,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"526","last_page":"531"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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":0.9998000264167786,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9976000189781189,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9927999973297119,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7433934807777405},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7415245175361633},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.7056689262390137},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6651516556739807},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.6337069272994995},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.6228545904159546},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5649595856666565},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.557807445526123},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5157149434089661},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.5057709217071533},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.48355838656425476},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4562174081802368},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.45433953404426575},{"id":"https://openalex.org/keywords/variation","display_name":"Variation (astronomy)","score":0.43270188570022583},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.42328014969825745},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4087563157081604},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3837797939777374},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07717850804328918}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7433934807777405},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7415245175361633},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.7056689262390137},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6651516556739807},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6337069272994995},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6228545904159546},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5649595856666565},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.557807445526123},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5157149434089661},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.5057709217071533},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.48355838656425476},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4562174081802368},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.45433953404426575},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.43270188570022583},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.42328014969825745},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4087563157081604},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3837797939777374},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07717850804328918},{"id":"https://openalex.org/C44870925","wikidata":"https://www.wikidata.org/wiki/Q37547","display_name":"Astrophysics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3529836.3529854","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3529836.3529854","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3529836.3529854","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3529836.3529854","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3529836.3529854","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3529836.3529854","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.5299999713897705,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4283276033.pdf","grobid_xml":"https://content.openalex.org/works/W4283276033.grobid-xml"},"referenced_works_count":14,"referenced_works":["https://openalex.org/W1483870316","https://openalex.org/W1487583988","https://openalex.org/W2102605133","https://openalex.org/W2129987527","https://openalex.org/W2194775991","https://openalex.org/W2274287116","https://openalex.org/W2549139847","https://openalex.org/W2607037079","https://openalex.org/W4301409532","https://openalex.org/W6631782140","https://openalex.org/W6639102338","https://openalex.org/W6658049321","https://openalex.org/W6718379498","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W2888032422","https://openalex.org/W4385421777","https://openalex.org/W4377980832","https://openalex.org/W2897769091","https://openalex.org/W2845413374","https://openalex.org/W3005996785","https://openalex.org/W4297411772","https://openalex.org/W4235873501"],"abstract_inverted_index":{"Abstract:":[0],"Performing":[1],"object":[2,110,135],"detection":[3,111],"on":[4,150,169],"partially":[5,126],"occluded":[6,42,170],"objects":[7],"is":[8,33,49,73],"a":[9,36,70,117],"challenging":[10,132],"task":[11],"due":[12],"to":[13,30,34,104,137],"the":[14,53,61,86,99,106,134,141,151,156,159,166],"amount":[15,54],"of":[16,55,82,101,108,124,143,161],"variation":[17],"in":[18,24,60,85,164],"location,":[19],"scale,":[20],"and":[21,57,155],"ratio":[22],"present":[23],"real-world":[25],"occlusion.":[26],"A":[27],"typical":[28],"solution":[29],"this":[31,48,90],"problem":[32],"provide":[35],"large":[37],"enough":[38],"dataset":[39],"with":[40,68],"ample":[41],"samples":[43,129],"for":[44,133],"feature":[45],"learning.":[46],"However,":[47],"rather":[50],"costly":[51],"given":[52],"time":[56],"effort":[58],"involved":[59],"data":[62],"collection":[63],"process.":[64],"In":[65,89],"addition,":[66],"even":[67],"such":[69,144],"dataset,":[71],"there":[72],"no":[74],"guarantee":[75],"that":[76,97,130],"it":[77],"covers":[78],"all":[79],"possible":[80],"cases":[81],"common":[83,109],"occlusion":[84],"real":[87],"world.":[88],"paper,":[91],"we":[92,115],"propose":[93,116],"an":[94,145],"alternate":[95],"approach":[96,146,163],"utilizes":[98],"power":[100],"adversarial":[102],"learning":[103],"reinforce":[105],"training":[107,128],"models.":[112],"More":[113],"specifically,":[114],"Generative":[118],"Adversarial":[119],"Occlusion":[120],"Network":[121],"(GAON)":[122],"capable":[123],"generating":[125],"shaded":[127],"are":[131],"detector":[136],"classify.":[138],"We":[139],"demonstrate":[140],"efficacy":[142],"by":[147],"conducting":[148],"experiments":[149],"Faster":[152],"R-CNN":[153],"detector,":[154],"results":[157],"indicate":[158],"superiority":[160],"our":[162],"improving":[165],"model's":[167],"performance":[168],"inputs.":[171]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}