{"id":"https://openalex.org/W3016221450","doi":"https://doi.org/10.1109/icassp40776.2020.9054502","title":"Effective Pipeline for Compressing Deep Object Detectors","display_name":"Effective Pipeline for Compressing Deep Object Detectors","publication_year":2020,"publication_date":"2020-04-09","ids":{"openalex":"https://openalex.org/W3016221450","doi":"https://doi.org/10.1109/icassp40776.2020.9054502","mag":"3016221450"},"language":"en","primary_location":{"id":"doi:10.1109/icassp40776.2020.9054502","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9054502","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5019176355","display_name":"Yiwu Yao","orcid":"https://orcid.org/0000-0003-4642-5840"},"institutions":[{"id":"https://openalex.org/I168879160","display_name":"Zhejiang University of Science and Technology","ror":"https://ror.org/05mx0wr29","country_code":"CN","type":"education","lineage":["https://openalex.org/I168879160"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiwu Yao","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I168879160"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064687463","display_name":"Zheng Fang","orcid":"https://orcid.org/0000-0003-3887-3141"},"institutions":[{"id":"https://openalex.org/I168879160","display_name":"Zhejiang University of Science and Technology","ror":"https://ror.org/05mx0wr29","country_code":"CN","type":"education","lineage":["https://openalex.org/I168879160"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Fang","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I168879160"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035683291","display_name":"Bin Dong","orcid":"https://orcid.org/0000-0003-1295-3362"},"institutions":[{"id":"https://openalex.org/I4210091137","display_name":"NetEase (China)","ror":"https://ror.org/00fp6fj05","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091137"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Dong","raw_affiliation_strings":["R&D Center, NetEase Inc, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"R&D Center, NetEase Inc, Hangzhou, China","institution_ids":["https://openalex.org/I4210091137"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081365930","display_name":"Sen Zhou","orcid":"https://orcid.org/0009-0002-3186-2060"},"institutions":[{"id":"https://openalex.org/I4210091137","display_name":"NetEase (China)","ror":"https://ror.org/00fp6fj05","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091137"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sen Zhou","raw_affiliation_strings":["R&D Center, NetEase Inc, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"R&D Center, NetEase Inc, Hangzhou, China","institution_ids":["https://openalex.org/I4210091137"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4472","last_page":"4476"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9991999864578247,"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":0.9986000061035156,"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/pipeline","display_name":"Pipeline (software)","score":0.8440191745758057},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.74554044008255},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.7380181550979614},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6618878841400146},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5607051253318787},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5259568691253662},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4548507332801819},{"id":"https://openalex.org/keywords/subnet","display_name":"Subnet","score":0.45310893654823303},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.44583064317703247},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4342499375343323},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4120786190032959},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3904831111431122},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.35813403129577637},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.248031884431839},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.09108364582061768},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.0892864465713501}],"concepts":[{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.8440191745758057},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.74554044008255},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.7380181550979614},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6618878841400146},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5607051253318787},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5259568691253662},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4548507332801819},{"id":"https://openalex.org/C21099817","wikidata":"https://www.wikidata.org/wiki/Q7631721","display_name":"Subnet","level":2,"score":0.45310893654823303},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.44583064317703247},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4342499375343323},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4120786190032959},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3904831111431122},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35813403129577637},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.248031884431839},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.09108364582061768},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0892864465713501},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp40776.2020.9054502","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9054502","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.6200000047683716}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1821462560","https://openalex.org/W2407521645","https://openalex.org/W2553910756","https://openalex.org/W2561238782","https://openalex.org/W2612445135","https://openalex.org/W2750784772","https://openalex.org/W2808168148","https://openalex.org/W2887447938","https://openalex.org/W2950800384","https://openalex.org/W2962851801","https://openalex.org/W2963122961","https://openalex.org/W2963342610","https://openalex.org/W2963351448","https://openalex.org/W2963363373","https://openalex.org/W2963786238","https://openalex.org/W2963856926","https://openalex.org/W2963918968","https://openalex.org/W2964228333","https://openalex.org/W2964233199","https://openalex.org/W2964299589","https://openalex.org/W3106250896","https://openalex.org/W4297775537","https://openalex.org/W6638523607","https://openalex.org/W6714138976","https://openalex.org/W6729763630","https://openalex.org/W6730179637","https://openalex.org/W6734062232","https://openalex.org/W6737664043","https://openalex.org/W6743188669","https://openalex.org/W6749810415","https://openalex.org/W6754259177","https://openalex.org/W6754273798","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W2102539527","https://openalex.org/W2131631951","https://openalex.org/W2356206668","https://openalex.org/W2130707537","https://openalex.org/W3200778902","https://openalex.org/W1977409556","https://openalex.org/W2361602549","https://openalex.org/W4362683600","https://openalex.org/W2105155969","https://openalex.org/W4394867575"],"abstract_inverted_index":{"To":[0],"alleviate":[1],"the":[2,28,52,56,62,75,89,108,117],"deployment":[3],"of":[4,35,55,64,80,124],"deep":[5],"object":[6,57],"detectors":[7],"with":[8,61,103,121],"large":[9],"model":[10,17,99,114],"capacity":[11],"and":[12,41,68,78],"complex":[13],"computation,":[14],"an":[15,46],"effective":[16],"compression":[18],"pipeline":[19,82,94,109],"is":[20],"designed":[21],"in":[22],"this":[23],"paper.":[24],"Firstly,":[25],"attributed":[26],"to":[27,44],"refined":[29],"soft":[30],"filter":[31],"pruning,":[32],"3D":[33],"filters":[34],"each":[36],"convolution":[37],"layer":[38],"are":[39,59],"regularized":[40],"then":[42],"auto-pruned":[43],"achieve":[45],"overall":[47],"more":[48,96,111],"compact":[49],"backbone.":[50],"Afterwards,":[51],"branch":[53],"layers":[54],"detector":[58,120],"simplified":[60],"usage":[63],"simple":[65],"residual":[66],"blocks":[67],"fixed":[69],"channel":[70],"deletion.":[71],"Experimental":[72],"results":[73],"reveal":[74],"superior":[76],"effectiveness":[77],"generality":[79],"proposed":[81,93],"for":[83],"compressing":[84],"detection":[85,91],"models.":[86],"Notably,":[87],"on":[88,101,116,127],"generic":[90],"dataset,":[92],"reduces":[95],"than":[97,112],"67%":[98],"size":[100,115],"RefineDet":[102],"negligible":[104],"mAP":[105],"loss.":[106],"Moreover,":[107],"decreases":[110],"73%":[113],"PyramidBox":[118],"face":[119],"little":[122],"loss":[123],"hard":[125],"AP":[126],"WIDER":[128],"FACE.":[129]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
