{"id":"https://openalex.org/W4402593854","doi":"https://doi.org/10.1109/avss61716.2024.10672565","title":"Self-Supervised Industrial Image Generation for Defect Recognition Under Limited Data","display_name":"Self-Supervised Industrial Image Generation for Defect Recognition Under Limited Data","publication_year":2024,"publication_date":"2024-07-15","ids":{"openalex":"https://openalex.org/W4402593854","doi":"https://doi.org/10.1109/avss61716.2024.10672565"},"language":"en","primary_location":{"id":"doi:10.1109/avss61716.2024.10672565","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/avss61716.2024.10672565","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","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/A5023927549","display_name":"Younkwan Lee","orcid":"https://orcid.org/0000-0001-5622-3383"},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Younkwan Lee","raw_affiliation_strings":["Global Technology Research Samsung Electronics"],"affiliations":[{"raw_affiliation_string":"Global Technology Research Samsung Electronics","institution_ids":["https://openalex.org/I2250650973"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5023927549"],"corresponding_institution_ids":["https://openalex.org/I2250650973"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20606287,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"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":0.9215999841690063,"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":0.7010159492492676},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6170406341552734},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5215020775794983},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49030497670173645},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.44413435459136963}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7010159492492676},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6170406341552734},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5215020775794983},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49030497670173645},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44413435459136963}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/avss61716.2024.10672565","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/avss61716.2024.10672565","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","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":35,"referenced_works":["https://openalex.org/W1968579151","https://openalex.org/W2172259569","https://openalex.org/W2779176852","https://openalex.org/W2895033814","https://openalex.org/W2945708832","https://openalex.org/W2948982773","https://openalex.org/W2962785568","https://openalex.org/W2962793481","https://openalex.org/W2964325192","https://openalex.org/W2966341653","https://openalex.org/W2967584026","https://openalex.org/W2982243647","https://openalex.org/W2989446488","https://openalex.org/W2991593724","https://openalex.org/W3006938707","https://openalex.org/W3024889357","https://openalex.org/W3096831136","https://openalex.org/W3107127161","https://openalex.org/W3120387510","https://openalex.org/W3170790383","https://openalex.org/W3174480022","https://openalex.org/W3206968309","https://openalex.org/W4214507781","https://openalex.org/W4221055893","https://openalex.org/W4226300688","https://openalex.org/W4285188260","https://openalex.org/W4311415873","https://openalex.org/W4312364770","https://openalex.org/W4312443924","https://openalex.org/W4313892909","https://openalex.org/W4375861378","https://openalex.org/W4382240612","https://openalex.org/W6757555829","https://openalex.org/W6779093361","https://openalex.org/W6780507278"],"related_works":["https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2772917594","https://openalex.org/W2775347418","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":{"Deep":[0],"learning-based":[1],"industrial":[2],"image":[3,72],"inspection":[4],"has":[5],"achieved":[6],"tremendous":[7],"success":[8],"in":[9,65],"recent":[10,128],"years,":[11],"while":[12],"the":[13,38,43,59,94,98,102,119,127],"task":[14],"of":[15],"learning":[16],"a":[17,20,52,79],"model":[18],"from":[19],"few":[21],"defect":[22,34,71,90,114],"images":[23,35],"remains":[24],"unexplored.":[25],"The":[26],"most":[27],"popular":[28],"method":[29],"is":[30],"to":[31,36,85,126],"directly":[32,86],"generate":[33],"augment":[37],"limited":[39],"dataset":[40],"before":[41],"training":[42],"recognition":[44],"model.":[45],"To":[46],"address":[47],"this":[48],"issue,":[49],"we":[50,77],"propose":[51],"new":[53],"framework":[54],"called":[55],"REG-Net":[56],"that":[57,123],"combines":[58],"generation":[60,73,103],"module":[61,64,81,100,104],"and":[62,89],"classification":[63,99,110],"an":[66],"end-to-end":[67],"manner,":[68],"with":[69,82],"few-shot":[70],"as":[74],"assistance.":[75],"Specifically,":[76],"design":[78],"two-branch":[80],"attention":[83],"fusion":[84],"combine":[87],"normal":[88],"features.":[91],"This":[92],"reduces":[93],"negative":[95],"impact":[96],"on":[97,118],"when":[101],"performs":[105],"poorly.":[106],"REGNet":[107],"improves":[108],"further":[109],"performance":[111],"through":[112],"recognition-friendly":[113],"generation.":[115],"Extensive":[116],"experiments":[117],"MVTec-AD":[120],"benchmark":[121],"show":[122],"effectiveness":[124],"compared":[125],"state-of-the-art":[129],"methods.":[130]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}