{"id":"https://openalex.org/W3091317905","doi":"https://doi.org/10.1109/icip40778.2020.9190661","title":"Active Image Sampling on Canonical Views for Novel Object Detection","display_name":"Active Image Sampling on Canonical Views for Novel Object Detection","publication_year":2020,"publication_date":"2020-09-30","ids":{"openalex":"https://openalex.org/W3091317905","doi":"https://doi.org/10.1109/icip40778.2020.9190661","mag":"3091317905"},"language":"en","primary_location":{"id":"doi:10.1109/icip40778.2020.9190661","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9190661","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Image Processing (ICIP)","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/A5052143634","display_name":"Qianli Xu","orcid":"https://orcid.org/0000-0003-0105-5903"},"institutions":[{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]},{"id":"https://openalex.org/I115228651","display_name":"Agency for Science, Technology and Research","ror":"https://ror.org/036wvzt09","country_code":"SG","type":"government","lineage":["https://openalex.org/I115228651"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Qianli Xu","raw_affiliation_strings":["Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore"],"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore","institution_ids":["https://openalex.org/I3005327000","https://openalex.org/I115228651"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030374805","display_name":"Fen Fang","orcid":"https://orcid.org/0000-0002-3834-4795"},"institutions":[{"id":"https://openalex.org/I115228651","display_name":"Agency for Science, Technology and Research","ror":"https://ror.org/036wvzt09","country_code":"SG","type":"government","lineage":["https://openalex.org/I115228651"]},{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Fen Fang","raw_affiliation_strings":["Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore"],"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore","institution_ids":["https://openalex.org/I3005327000","https://openalex.org/I115228651"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036812295","display_name":"Nicolas Gauthier","orcid":"https://orcid.org/0000-0002-2225-5827"},"institutions":[{"id":"https://openalex.org/I115228651","display_name":"Agency for Science, Technology and Research","ror":"https://ror.org/036wvzt09","country_code":"SG","type":"government","lineage":["https://openalex.org/I115228651"]},{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Nicolas Gauthier","raw_affiliation_strings":["Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore"],"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore","institution_ids":["https://openalex.org/I3005327000","https://openalex.org/I115228651"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101524913","display_name":"Liyuan Li","orcid":"https://orcid.org/0000-0002-3758-1975"},"institutions":[{"id":"https://openalex.org/I115228651","display_name":"Agency for Science, Technology and Research","ror":"https://ror.org/036wvzt09","country_code":"SG","type":"government","lineage":["https://openalex.org/I115228651"]},{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Liyuan Li","raw_affiliation_strings":["Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore"],"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore","institution_ids":["https://openalex.org/I3005327000","https://openalex.org/I115228651"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077258552","display_name":"Joo\u2010Hwee Lim","orcid":"https://orcid.org/0000-0002-4103-3824"},"institutions":[{"id":"https://openalex.org/I115228651","display_name":"Agency for Science, Technology and Research","ror":"https://ror.org/036wvzt09","country_code":"SG","type":"government","lineage":["https://openalex.org/I115228651"]},{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Joo-Hwee Lim","raw_affiliation_strings":["Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore"],"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore","institution_ids":["https://openalex.org/I3005327000","https://openalex.org/I115228651"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5052143634"],"corresponding_institution_ids":["https://openalex.org/I115228651","https://openalex.org/I3005327000"],"apc_list":null,"apc_paid":null,"fwci":0.6628,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.76213401,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2241","last_page":"2245"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9973999857902527,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9973999857902527,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9972000122070312,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9922999739646912,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7531728148460388},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.726677417755127},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6780457496643066},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6747995018959045},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.664268970489502},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5633082985877991},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.4721405804157257},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.42501455545425415},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35491806268692017}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7531728148460388},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.726677417755127},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6780457496643066},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6747995018959045},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.664268970489502},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5633082985877991},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.4721405804157257},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.42501455545425415},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35491806268692017},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip40778.2020.9190661","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9190661","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Image Processing (ICIP)","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":23,"referenced_works":["https://openalex.org/W334719179","https://openalex.org/W639708223","https://openalex.org/W1526868886","https://openalex.org/W2020327719","https://openalex.org/W2079745210","https://openalex.org/W2086479969","https://openalex.org/W2107031757","https://openalex.org/W2111421727","https://openalex.org/W2120995529","https://openalex.org/W2345663917","https://openalex.org/W2580726517","https://openalex.org/W2613718673","https://openalex.org/W2788515346","https://openalex.org/W2795373741","https://openalex.org/W2797868916","https://openalex.org/W2803694688","https://openalex.org/W2962837320","https://openalex.org/W2963845150","https://openalex.org/W2996583130","https://openalex.org/W3005169857","https://openalex.org/W4231674576","https://openalex.org/W6631711059","https://openalex.org/W6750055409"],"related_works":["https://openalex.org/W4238897586","https://openalex.org/W435179959","https://openalex.org/W2619091065","https://openalex.org/W2059640416","https://openalex.org/W1490753184","https://openalex.org/W2284465472","https://openalex.org/W2291782699","https://openalex.org/W1993948687","https://openalex.org/W2000169967","https://openalex.org/W2963610131"],"abstract_inverted_index":{"To":[0],"alleviate":[1],"the":[2,14,24,30,49,56,60,127,135,141,150],"costly":[3],"data":[4,118,123],"annotation":[5],"problem":[6],"in":[7,59],"deep":[8],"learning-based":[9],"object":[10,52,67,95],"detection,":[11,96],"we":[12,33,72,109],"leverage":[13],"canonical":[15,40],"view":[16],"model":[17,115],"for":[18,65],"active":[19],"sample":[20],"selection":[21],"to":[22,104],"improve":[23],"effectiveness":[25,50],"of":[26,45,51,62,88,93],"learning.":[27,53],"Inspired":[28],"by":[29,85,126],"view-approximation":[31],"model,":[32],"hypothesize":[34],"that":[35,81,140],"visual":[36,89],"features":[37,90],"learned":[38],"from":[39],"views":[41],"denote":[42],"better":[43],"representations":[44],"objects,":[46],"thus":[47],"boosting":[48],"We":[54,130],"validate":[55],"hypothesis":[57],"empirically":[58],"context":[61],"robot":[63],"learning":[64,106],"novel":[66,75],"detection.":[68],"Based":[69],"on":[70,134],"this,":[71],"propose":[73],"a":[74,111,117],"on-line":[76],"viewpoint":[77],"exploration":[78],"(OLIVE)":[79],"method":[80,120,133,143],"(1)":[82],"defines":[83],"goodness-of-view":[84],"combining":[86],"informativeness":[87],"and":[91,97,101,138],"consistency":[92],"model-based":[94],"(2)":[98],"systematically":[99],"explores":[100],"selects":[102],"viewpoints":[103],"boost":[105],"efficiency.":[107],"Furthermore,":[108],"train":[110],"legacy":[112],"Faster":[113],"R-CNN":[114],"with":[116],"augmentation":[119],"while":[121],"leveraging":[122],"samples":[124,151],"generated":[125],"OLIVE":[128],"pipeline.":[129],"test":[131],"our":[132],"T-LESS":[136],"dataset":[137],"show":[139],"proposed":[142],"outperforms":[144],"competitive":[145],"benchmarking":[146],"methods,":[147],"especially":[148],"when":[149],"are":[152],"few.":[153]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4}],"updated_date":"2025-11-25T21:42:39.735039","created_date":"2025-10-10T00:00:00"}