iBet uBet
web content aggregator. Adding the entire web to your favor.
Link to original content:
https://api.openalex.org/works/doi:10.1109/ICIP42928.2021.9506202
{"id":"https://openalex.org/W3193713796","doi":"https://doi.org/10.1109/icip42928.2021.9506202","title":"A One-Shot Texture-Perceiving Generative Adversarial Network for Unsupervised Surface Inspection","display_name":"A One-Shot Texture-Perceiving Generative Adversarial Network for Unsupervised Surface Inspection","publication_year":2021,"publication_date":"2021-08-23","ids":{"openalex":"https://openalex.org/W3193713796","doi":"https://doi.org/10.1109/icip42928.2021.9506202","mag":"3193713796"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip42928.2021.9506202","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://arxiv.org/pdf/2106.06792","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102938179","display_name":"Lingyun Gu","orcid":"https://orcid.org/0000-0003-1105-5835"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingyun Gu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100418969","display_name":"Zhang Li","orcid":"https://orcid.org/0000-0003-1989-6102"},"institutions":[{"id":"https://openalex.org/I63135867","display_name":"University of Cincinnati","ror":"https://ror.org/01e3m7079","country_code":"US","type":"education","lineage":["https://openalex.org/I63135867"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lin Zhang","raw_affiliation_strings":["University of Cincinnati, Cincinnati, Ohio, USA"],"affiliations":[{"raw_affiliation_string":"University of Cincinnati, Cincinnati, Ohio, USA","institution_ids":["https://openalex.org/I63135867"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102881552","display_name":"Zhaokui Wang","orcid":"https://orcid.org/0000-0003-2568-7948"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaokui Wang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institution_assertions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.088,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.863079,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":72,"max":76},"biblio":{"volume":null,"issue":null,"first_page":"1519","last_page":"1523"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9995,"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":0.9995,"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/T10638","display_name":"Optical measurement and interference techniques","score":0.9923,"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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9893,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.8007562},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.60546464},{"id":"https://openalex.org/keywords/representation","display_name":"Representation","score":0.5795791},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.50669295},{"id":"https://openalex.org/keywords/texture","display_name":"Texture (cosmology)","score":0.47350317}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.8007562},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7644111},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7597315},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.60546464},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5833153},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5795791},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5499331},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.50669295},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.501143},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.49832416},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.49078426},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.48360935},{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.47350317},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.46139264},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12882516},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip42928.2021.9506202","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2106.06792","pdf_url":"http://arxiv.org/pdf/2106.06792","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2106.06792","pdf_url":"http://arxiv.org/pdf/2106.06792","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.57,"id":"https://metadata.un.org/sdg/10"}],"grants":[{"funder":"https://openalex.org/F4320321133","funder_display_name":"Chinese Academy of Sciences","award_id":null}],"datasets":[],"versions":[],"referenced_works_count":18,"referenced_works":["https://openalex.org/W1988299413","https://openalex.org/W1988670984","https://openalex.org/W2072994056","https://openalex.org/W2074925468","https://openalex.org/W2746363676","https://openalex.org/W2762457108","https://openalex.org/W2766183093","https://openalex.org/W2801289723","https://openalex.org/W2884195426","https://openalex.org/W2963299736","https://openalex.org/W2964309882","https://openalex.org/W2982041717","https://openalex.org/W2990069792","https://openalex.org/W3093025987","https://openalex.org/W3099088591","https://openalex.org/W3104156061","https://openalex.org/W3174505228","https://openalex.org/W3189423081"],"related_works":["https://openalex.org/W4322709305","https://openalex.org/W4308928038","https://openalex.org/W4200430540","https://openalex.org/W3217069185","https://openalex.org/W3141413246","https://openalex.org/W3049340819","https://openalex.org/W2996316059","https://openalex.org/W2995777218","https://openalex.org/W2888032422","https://openalex.org/W2808862658"],"abstract_inverted_index":{"Visual":[0],"surface":[1,100],"inspection":[2],"is":[3,38,62,113],"a":[4,42,54,77,110,136],"challenging":[5],"task":[6],"owing":[7],"to":[8,40,115,131],"the":[9,65,74,85,108,117,142],"highly":[10],"diverse":[11],"appearance":[12],"of":[13,26,45,79,91,121,138,144],"target":[14],"surfaces":[15],"and":[16,88],"defective":[17,99,132],"regions.":[18],"Previous":[19],"attempts":[20],"heavily":[21],"rely":[22],"on":[23,135],"vast":[24],"quantities":[25],"training":[27],"examples":[28],"with":[29],"manual":[30],"annotation.":[31],"However,":[32],"in":[33,69,107],"some":[34],"practical":[35],"cases,":[36],"it":[37,128],"difficult":[39],"obtain":[41],"large":[43],"number":[44],"samples":[46],"for":[47],"inspection.":[48],"To":[49],"combat":[50],"it,":[51],"we":[52],"propose":[53],"hierarchical":[55],"texture-perceiving":[56,111],"generative":[57],"adversarial":[58],"network":[59],"(HTPGAN)":[60],"that":[61,82],"learned":[63],"from":[64,102],"one-shot":[66],"normal":[67,103,122],"image":[68,93,123],"an":[70,92],"unsupervised":[71],"scheme.":[72],"Specifically,":[73],"HTP-GAN":[75],"contains":[76],"pyramid":[78],"convolutional":[80],"GANs":[81],"can":[83],"capture":[84,116],"global":[86],"structure":[87],"fine-grained":[89],"representation":[90,120],"simultaneously.":[94],"This":[95],"innovation":[96],"helps":[97],"distinguishing":[98],"regions":[101],"ones.":[104],"In":[105],"addition,":[106],"discriminator,":[109],"module":[112],"devised":[114],"spatially":[118],"invariant":[119],"via":[124],"directional":[125],"convolutions,":[126],"making":[127],"more":[129],"sensitive":[130],"areas.":[133],"Experiments":[134],"variety":[137],"datasets":[139],"consistently":[140],"demonstrate":[141],"effectiveness":[143],"our":[145],"method.":[146]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3193713796","counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2024-12-08T02:58:33.291210","created_date":"2021-08-30"}
