{"id":"https://openalex.org/W3191321386","doi":"https://doi.org/10.1145/3447993.3483274","title":"Flexible high-resolution object detection on edge devices with tunable latency","display_name":"Flexible high-resolution object detection on edge devices with tunable latency","publication_year":2021,"publication_date":"2021-10-25","ids":{"openalex":"https://openalex.org/W3191321386","doi":"https://doi.org/10.1145/3447993.3483274","mag":"3191321386"},"language":"en","primary_location":{"id":"doi:10.1145/3447993.3483274","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447993.3483274","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th Annual International Conference on Mobile Computing and Networking","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/A5101989690","display_name":"Shiqi Jiang","orcid":"https://orcid.org/0000-0002-4685-9633"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Shiqi Jiang","raw_affiliation_strings":["Microsoft Research"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027752718","display_name":"Zhiqi Lin","orcid":"https://orcid.org/0000-0002-8050-6196"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiqi Lin","raw_affiliation_strings":["University of Science and Technology of China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100628298","display_name":"Yuanchun Li","orcid":"https://orcid.org/0000-0002-1591-2526"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yuanchun Li","raw_affiliation_strings":["Microsoft Research"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016472874","display_name":"Yuanchao Shu","orcid":"https://orcid.org/0000-0002-9542-7095"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yuanchao Shu","raw_affiliation_strings":["Microsoft Research"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102880548","display_name":"Yunxin Liu","orcid":"https://orcid.org/0000-0001-7352-8955"},"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":"Yunxin Liu","raw_affiliation_strings":["Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101989690"],"corresponding_institution_ids":["https://openalex.org/I4210164937"],"apc_list":null,"apc_paid":null,"fwci":7.013,"has_fulltext":false,"cited_by_count":96,"citation_normalized_percentile":{"value":0.97879085,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"559","last_page":"572"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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/T11105","display_name":"Advanced Image Processing 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"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9957000017166138,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8497470617294312},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.7041587233543396},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6227979063987732},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.5873043537139893},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5848353505134583},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5284309983253479},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4957009553909302},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4736248850822449},{"id":"https://openalex.org/keywords/edge-computing","display_name":"Edge computing","score":0.4586527347564697},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.45616015791893005},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.43406862020492554},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.41518479585647583},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3852289915084839},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.35243988037109375},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.3225739300251007},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.25237393379211426},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.13533559441566467},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.12534019351005554},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09849292039871216},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.09781220555305481}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8497470617294312},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.7041587233543396},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6227979063987732},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.5873043537139893},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5848353505134583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5284309983253479},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4957009553909302},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4736248850822449},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.4586527347564697},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.45616015791893005},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.43406862020492554},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.41518479585647583},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3852289915084839},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35243988037109375},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.3225739300251007},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25237393379211426},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.13533559441566467},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.12534019351005554},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09849292039871216},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.09781220555305481},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3447993.3483274","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447993.3483274","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th Annual International Conference on Mobile Computing and Networking","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":45,"referenced_works":["https://openalex.org/W41554520","https://openalex.org/W95608104","https://openalex.org/W639708223","https://openalex.org/W1583837637","https://openalex.org/W1969591148","https://openalex.org/W2037227137","https://openalex.org/W2144506857","https://openalex.org/W2147800946","https://openalex.org/W2336589871","https://openalex.org/W2468875367","https://openalex.org/W2525951180","https://openalex.org/W2553915786","https://openalex.org/W2625157458","https://openalex.org/W2626129225","https://openalex.org/W2747329762","https://openalex.org/W2789383786","https://openalex.org/W2798930779","https://openalex.org/W2829720336","https://openalex.org/W2883429621","https://openalex.org/W2889402930","https://openalex.org/W2897268228","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W2963585656","https://openalex.org/W2964137095","https://openalex.org/W2964233199","https://openalex.org/W2972684890","https://openalex.org/W3022536434","https://openalex.org/W3033997960","https://openalex.org/W3080230561","https://openalex.org/W3080950018","https://openalex.org/W3088076788","https://openalex.org/W3101962329","https://openalex.org/W3103203826","https://openalex.org/W3104331387","https://openalex.org/W3114753236","https://openalex.org/W3118035304","https://openalex.org/W3144271226","https://openalex.org/W3147042931","https://openalex.org/W3147954149","https://openalex.org/W4236853429","https://openalex.org/W4240805545","https://openalex.org/W4241645694","https://openalex.org/W4255556797","https://openalex.org/W4286696412"],"related_works":["https://openalex.org/W4378806073","https://openalex.org/W3162668736","https://openalex.org/W4281678247","https://openalex.org/W2736305332","https://openalex.org/W3131458535","https://openalex.org/W2949866693","https://openalex.org/W4308342181","https://openalex.org/W4362473790","https://openalex.org/W4293261943","https://openalex.org/W3103213585"],"abstract_inverted_index":{"Object":[0],"detection":[1,16,70,109,147],"is":[2],"a":[3,64,78,103,152],"fundamental":[4],"building":[5],"block":[6],"of":[7,50,115,121],"video":[8,53],"analytics":[9],"applications.":[10],"While":[11],"Neural":[12],"Networks":[13],"(NNs)-based":[14],"object":[15,69],"models":[17],"have":[18],"shown":[19],"excellent":[20],"accuracy":[21,110,148,164],"on":[22,34,71,97,127,165],"benchmark":[23],"datasets,":[24],"they":[25],"are":[26],"not":[27],"well":[28,131],"positioned":[29],"for":[30,67,151],"high-resolution":[31,68],"images":[32],"inference":[33,161],"resource-constrained":[35],"edge":[36,72],"devices.":[37,73],"Common":[38],"approaches,":[39],"including":[40],"down-sampling":[41],"inputs":[42],"and":[43,56,81,88],"scaling":[44],"up":[45,83,158],"neural":[46,95],"networks,":[47],"fall":[48],"short":[49],"adapting":[51],"to":[52,159],"content":[54],"changes":[55],"various":[57,113],"latency":[58,79,154],"requirements.":[59],"This":[60],"paper":[61],"presents":[62],"Remix,":[63],"flexible":[65],"framework":[66],"Remix":[74,126,142],"takes":[75],"as":[76,130,132],"input":[77],"budget,":[80,155],"come":[82],"with":[84,163,167],"an":[85,122],"image":[86,100],"partition":[87],"model":[89],"execution":[90],"plan":[91],"which":[92],"runs":[93],"off-the-shelf":[94],"networks":[96],"non-uniformly":[98],"partitioned":[99],"blocks.":[101],"As":[102],"result,":[104],"it":[105],"maximizes":[106],"the":[107,146,168],"overall":[108],"by":[111,136,149],"allocating":[112],"amount":[114],"compute":[116],"power":[117],"onto":[118],"different":[119],"areas":[120],"image.":[123],"We":[124],"evaluate":[125],"public":[128],"dataset":[129],"real-world":[133],"videos":[134],"collected":[135],"ourselves.":[137],"Experimental":[138],"results":[139],"show":[140],"that":[141],"can":[143],"either":[144],"improve":[145],"18%-120%":[150],"given":[153],"or":[156],"achieve":[157],"8.1\u00d7":[160],"speedup":[162],"par":[166],"state-of-the-art":[169],"NNs.":[170]},"counts_by_year":[{"year":2025,"cited_by_count":23},{"year":2024,"cited_by_count":31},{"year":2023,"cited_by_count":30},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}