{"id":"https://openalex.org/W4372272180","doi":"https://doi.org/10.1145/3583120.3589809","title":"Demo Abstract: BiGuide: A Bi-level Data Acquisition Guidance for Object Detection on Mobile Devices","display_name":"Demo Abstract: BiGuide: A Bi-level Data Acquisition Guidance for Object Detection on Mobile Devices","publication_year":2023,"publication_date":"2023-05-05","ids":{"openalex":"https://openalex.org/W4372272180","doi":"https://doi.org/10.1145/3583120.3589809"},"language":"en","primary_location":{"id":"doi:10.1145/3583120.3589809","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583120.3589809","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 22nd International Conference on Information Processing in Sensor Networks","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/A5016695945","display_name":"Lin Duan","orcid":"https://orcid.org/0000-0001-8515-6351"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Lin Duan","raw_affiliation_strings":["Duke University, USA"],"affiliations":[{"raw_affiliation_string":"Duke University, USA","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018544702","display_name":"Ying Chen","orcid":"https://orcid.org/0000-0002-5086-9690"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ying Chen","raw_affiliation_strings":["Duke University, United States"],"affiliations":[{"raw_affiliation_string":"Duke University, United States","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036726336","display_name":"Maria Gorlatova","orcid":"https://orcid.org/0000-0002-5477-7830"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maria Gorlatova","raw_affiliation_strings":["Duke University, United States"],"affiliations":[{"raw_affiliation_string":"Duke University, United States","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5016695945"],"corresponding_institution_ids":["https://openalex.org/I170897317"],"apc_list":null,"apc_paid":null,"fwci":0.123,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.37283777,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"368","last_page":"369"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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":0.9995999932289124,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9973000288009644,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9970999956130981,"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":0.8105342984199524},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6885827779769897},{"id":"https://openalex.org/keywords/mobile-phone","display_name":"Mobile phone","score":0.45174407958984375},{"id":"https://openalex.org/keywords/data-collection","display_name":"Data collection","score":0.4472174644470215},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4186558127403259},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.403475284576416},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3559878468513489},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.33935946226119995},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33586928248405457}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8105342984199524},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6885827779769897},{"id":"https://openalex.org/C2777421447","wikidata":"https://www.wikidata.org/wiki/Q17517","display_name":"Mobile phone","level":2,"score":0.45174407958984375},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.4472174644470215},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4186558127403259},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.403475284576416},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3559878468513489},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.33935946226119995},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33586928248405457},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3583120.3589809","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583120.3589809","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 22nd International Conference on Information Processing in Sensor Networks","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5757163444","display_name":null,"funder_award_id":"CSR-1903136; CNS-1908051; IS-2046072","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320308737","display_name":"Facebook","ror":"https://ror.org/01zbnvs85"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W2980692729","https://openalex.org/W3162743447","https://openalex.org/W4285815152"],"related_works":["https://openalex.org/W2770593030","https://openalex.org/W4281727072","https://openalex.org/W4312219546","https://openalex.org/W3154990682","https://openalex.org/W2171975302","https://openalex.org/W2022352247","https://openalex.org/W2186048469","https://openalex.org/W1589637664","https://openalex.org/W2560201613","https://openalex.org/W1532073221"],"abstract_inverted_index":{"Real-time":[0],"object":[1,158],"detection":[2,173],"(OD)":[3],"is":[4,37,68,92,99],"a":[5,10,25,31,69,122,170],"key":[6],"enabling":[7],"technology":[8],"for":[9,73,113,128,169,223],"wide":[11],"range":[12],"of":[13,46,77,86,108,166,190,207,214,235],"emerging":[14],"mobile":[15,195],"system":[16],"applications.":[17],"However,":[18],"deploying":[19,179],"an":[20,163,221],"OD":[21,63,78,129],"model":[22,64,114,153],"pre-trained":[23],"on":[24,65],"public":[26],"dataset":[27,53],"(source":[28],"domain)":[29,36],"in":[30,80],"specific":[32],"local":[33,58],"environment":[34],"(target":[35],"known":[38],"to":[39,41,88,101,131,134,141,147],"lead":[40],"significant":[42,106],"performance":[43],"degradation":[44],"because":[45],"the":[47,52,55,62,75,84,149,181,194,198,204,208,212,215,233],"so-called":[48],"domain":[49],"gap":[50],"between":[51],"and":[54,60,157,178,201,229],"environment.":[56],"Collecting":[57],"data":[59,67,91,97,112,125,150,168],"fine-tuning":[61],"this":[66,90,117],"commonly":[70],"used":[71],"approach":[72],"improving":[74],"robustness":[76],"models":[79],"real-world":[81],"deployments.":[82],"Yet,":[83],"question":[85],"how":[87,224],"collect":[89],"currently":[93],"largely":[94],"overlooked;":[95],"unsupported":[96],"collection":[98],"likely":[100],"produce":[102],"datasets":[103],"that":[104,210],"contain":[105],"proportion":[107],"redundant":[109],"or":[110,139,145],"uninformative":[111],"training.":[115],"In":[116],"demo,":[118],"we":[119,175],"present":[120],"BiGuide,":[121],"bi-level":[123,230],"image":[124,226],"acquisition":[126],"guidance":[127,200,231],"tasks,":[130],"guide":[132],"users":[133],"change":[135],"their":[136],"camera":[137],"locations":[138],"angles":[140],"different":[142],"extents":[143],"(significantly":[144],"slightly)":[146],"obtain":[148],"which":[151],"benefits":[152],"training":[154],"via":[155],"image-level":[156],"instance-level":[159],"guidance.":[160],"We":[161],"showcase":[162],"interactive":[164],"demonstration":[165],"collecting":[167],"lemur":[171,191],"species":[172],"application":[174],"are":[176],"developing":[177],"at":[180],"Duke":[182],"Lemur":[183],"Center.":[184],"Demo":[185],"participants":[186],"will":[187,202,219],"take":[188],"pictures":[189],"toys":[192],"with":[193],"phone":[196],"under":[197],"real-time":[199,205,225],"observe":[203],"display":[206],"metrics":[209],"assess":[211],"importance":[213,227],"captured":[216],"data.":[217,237],"They":[218],"develop":[220],"intuition":[222],"assessment":[228],"improve":[232],"quality":[234],"collected":[236]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
