{"id":"https://openalex.org/W4391710124","doi":"https://doi.org/10.48550/arxiv.2402.05937","title":"InstaGen: Enhancing Object Detection by Training on Synthetic Dataset","display_name":"InstaGen: Enhancing Object Detection by Training on Synthetic Dataset","publication_year":2024,"publication_date":"2024-02-08","ids":{"openalex":"https://openalex.org/W4391710124","doi":"https://doi.org/10.48550/arxiv.2402.05937"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2402.05937","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2402.05937","pdf_url":"https://arxiv.org/pdf/2402.05937","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2402.05937","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101303327","display_name":"Chengjian Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Feng, Chengjian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028886830","display_name":"Yujie Zhong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhong, Yujie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075329194","display_name":"Zequn Jie","orcid":"https://orcid.org/0000-0002-3038-5891"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jie, Zequn","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076097168","display_name":"Weidi Xie","orcid":"https://orcid.org/0000-0003-3804-2639"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Weidi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100324184","display_name":"Lin Ma","orcid":"https://orcid.org/0000-0002-9491-7789"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Lin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101303327"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9639999866485596,"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":0.9639999866485596,"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/training","display_name":"Training (meteorology)","score":0.6947835683822632},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5863431096076965},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5523463487625122},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5409207940101624},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5081439018249512},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4359118342399597},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3867315649986267},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3450552821159363},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33226683735847473},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.13608548045158386}],"concepts":[{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6947835683822632},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5863431096076965},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5523463487625122},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5409207940101624},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5081439018249512},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4359118342399597},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3867315649986267},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3450552821159363},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33226683735847473},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.13608548045158386},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2402.05937","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2402.05937","pdf_url":"https://arxiv.org/pdf/2402.05937","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},{"id":"doi:10.48550/arxiv.2402.05937","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2402.05937","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2402.05937","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2402.05937","pdf_url":"https://arxiv.org/pdf/2402.05937","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4391710124.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W230091440","https://openalex.org/W2233261550","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2997094352","https://openalex.org/W3216976533","https://openalex.org/W100620283","https://openalex.org/W2495260952","https://openalex.org/W4292830139","https://openalex.org/W2551249631"],"abstract_inverted_index":{"In":[0],"this":[1,106],"paper,":[2],"we":[3,32],"present":[4],"a":[5,39,87,118],"novel":[6,88],"paradigm":[7],"to":[8,44,62,103,121,145],"enhance":[9,122],"the":[10,48,54,64,71,76,97],"ability":[11,49],"of":[12,50,67,75,109],"object":[13,84,123],"detector,":[14,85],"e.g.,":[15],"expanding":[16],"categories":[17,93],"or":[18],"improving":[19],"detection":[20],"performance,":[21],"by":[22,96,125],"training":[23,126],"on":[24,91,127],"synthetic":[25],"dataset":[26],"generated":[27,55,129],"from":[28,81],"diffusion":[29,42,77,110],"models.":[30],"Specifically,":[31],"integrate":[33],"an":[34,82],"instance-level":[35],"grounding":[36,58],"head":[37,59],"into":[38],"pre-trained,":[40],"generative":[41],"model,":[43,78,111],"augment":[45],"it":[46],"with":[47,70,151],"localising":[51],"instances":[52],"in":[53,138],"images.":[56],"The":[57],"is":[60],"trained":[61],"align":[63],"text":[65],"embedding":[66],"category":[68],"names":[69],"regional":[72],"visual":[73],"feature":[74],"using":[79],"supervision":[80],"off-the-shelf":[83],"and":[86,142],"self-training":[89],"scheme":[90],"(novel)":[92],"not":[94],"covered":[95],"detector.":[98],"We":[99],"conduct":[100],"thorough":[101],"experiments":[102],"show":[104],"that,":[105],"enhanced":[107],"version":[108],"termed":[112],"as":[113,117],"InstaGen,":[114],"can":[115],"serve":[116],"data":[119],"synthesizer,":[120],"detectors":[124],"its":[128],"samples,":[130],"demonstrating":[131],"superior":[132],"performance":[133],"over":[134],"existing":[135],"state-of-the-art":[136],"methods":[137],"open-vocabulary":[139],"(+4.5":[140],"AP)":[141,147],"data-sparse":[143],"(+1.2":[144],"5.2":[146],"scenarios.":[148],"Project":[149],"page":[150],"code:":[152],"https://fcjian.github.io/InstaGen.":[153]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}