{"id":"https://openalex.org/W3196948083","doi":"https://doi.org/10.1145/3460426.3463654","title":"Squeeze-and-Excitation network-Based Radar Object Detection With Weighted Location Fusion","display_name":"Squeeze-and-Excitation network-Based Radar Object Detection With Weighted Location Fusion","publication_year":2021,"publication_date":"2021-08-24","ids":{"openalex":"https://openalex.org/W3196948083","doi":"https://doi.org/10.1145/3460426.3463654","mag":"3196948083"},"language":"en","primary_location":{"id":"doi:10.1145/3460426.3463654","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460426.3463654","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 International Conference on Multimedia Retrieval","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/A5062688576","display_name":"Pengliang Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I202697423","display_name":"University of Zurich","ror":"https://ror.org/02crff812","country_code":"CH","type":"education","lineage":["https://openalex.org/I202697423"]},{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]},{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH","CN"],"is_corresponding":true,"raw_author_name":"Pengliang Sun","raw_affiliation_strings":["The Chinese University of Hong Kong, Hongkong, China","University of Zurich and ETH Zurich, Zurich, Switzerland"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hongkong, China","institution_ids":["https://openalex.org/I177725633"]},{"raw_affiliation_string":"University of Zurich and ETH Zurich, Zurich, Switzerland","institution_ids":["https://openalex.org/I35440088","https://openalex.org/I202697423"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049712995","display_name":"Xuetong Niu","orcid":null},"institutions":[{"id":"https://openalex.org/I183935753","display_name":"King's College London","ror":"https://ror.org/0220mzb33","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I183935753"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Xuetong Niu","raw_affiliation_strings":["King's College London, London, United Kingdom"],"affiliations":[{"raw_affiliation_string":"King's College London, London, United Kingdom","institution_ids":["https://openalex.org/I183935753"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100452233","display_name":"Pengfei Sun","orcid":"https://orcid.org/0000-0002-1855-5974"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]},{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]},{"id":"https://openalex.org/I202697423","display_name":"University of Zurich","ror":"https://ror.org/02crff812","country_code":"CH","type":"education","lineage":["https://openalex.org/I202697423"]}],"countries":["CH","CN"],"is_corresponding":false,"raw_author_name":"Pengfei Sun","raw_affiliation_strings":["The Chinese University of Hong Kong, Hongkong, China","University of Zurich and ETH Zurich, Zurich, Switzerland"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hongkong, China","institution_ids":["https://openalex.org/I177725633"]},{"raw_affiliation_string":"University of Zurich and ETH Zurich, Zurich, Switzerland","institution_ids":["https://openalex.org/I35440088","https://openalex.org/I202697423"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013340793","display_name":"Kele Xu","orcid":"https://orcid.org/0000-0001-5997-5169"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kele Xu","raw_affiliation_strings":["National Key Laboratory of Parallel and Distributed Processing, Changsha, China"],"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Parallel and Distributed Processing, Changsha, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5062688576"],"corresponding_institution_ids":["https://openalex.org/I177725633","https://openalex.org/I202697423","https://openalex.org/I35440088"],"apc_list":null,"apc_paid":null,"fwci":2.72,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.90222387,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"545","last_page":"552"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9976000189781189,"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"}},"topics":[{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9976000189781189,"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"}},{"id":"https://openalex.org/T11609","display_name":"Geophysical Methods and Applications","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T10036","display_name":"Advanced Neural Network Applications","score":0.9943000078201294,"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":0.8057289123535156},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.7104629278182983},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.647621750831604},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6443334817886353},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.6424493193626404},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5782900452613831},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5051479935646057},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5019540786743164},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4807814955711365},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.47860807180404663},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4751938581466675},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4736969470977783},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.4467056691646576},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4408625364303589},{"id":"https://openalex.org/keywords/radar-imaging","display_name":"Radar imaging","score":0.420732319355011},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41994109749794006},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3866216540336609},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34568917751312256},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34542304277420044},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.19307371973991394},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.11446505784988403},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09447664022445679}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8057289123535156},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.7104629278182983},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.647621750831604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6443334817886353},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.6424493193626404},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5782900452613831},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5051479935646057},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5019540786743164},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4807814955711365},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.47860807180404663},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4751938581466675},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4736969470977783},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.4467056691646576},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4408625364303589},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.420732319355011},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41994109749794006},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3866216540336609},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34568917751312256},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34542304277420044},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.19307371973991394},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.11446505784988403},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09447664022445679},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3460426.3463654","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460426.3463654","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 International Conference on Multimedia Retrieval","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":41,"referenced_works":["https://openalex.org/W2086355237","https://openalex.org/W2102605133","https://openalex.org/W2151103935","https://openalex.org/W2206167351","https://openalex.org/W2307770531","https://openalex.org/W2619697695","https://openalex.org/W2735992466","https://openalex.org/W2752782242","https://openalex.org/W2786768213","https://openalex.org/W2796574722","https://openalex.org/W2799062425","https://openalex.org/W2888252458","https://openalex.org/W2912269676","https://openalex.org/W2922420408","https://openalex.org/W2943515156","https://openalex.org/W2948490177","https://openalex.org/W2963091558","https://openalex.org/W2963249133","https://openalex.org/W2963292632","https://openalex.org/W2963351448","https://openalex.org/W2964444661","https://openalex.org/W2967957126","https://openalex.org/W2981648413","https://openalex.org/W2981709511","https://openalex.org/W2981851019","https://openalex.org/W2982220924","https://openalex.org/W3008308650","https://openalex.org/W3016223102","https://openalex.org/W3035172746","https://openalex.org/W3035574168","https://openalex.org/W3035730217","https://openalex.org/W3081236109","https://openalex.org/W3090139470","https://openalex.org/W3096767823","https://openalex.org/W3099090854","https://openalex.org/W3103179390","https://openalex.org/W3105073457","https://openalex.org/W3106651317","https://openalex.org/W3118479569","https://openalex.org/W3120815051","https://openalex.org/W6604947854"],"related_works":["https://openalex.org/W2385859805","https://openalex.org/W2530972254","https://openalex.org/W2374013449","https://openalex.org/W73545470","https://openalex.org/W2364381299","https://openalex.org/W2374430585","https://openalex.org/W3144423903","https://openalex.org/W2377397762","https://openalex.org/W2793967660","https://openalex.org/W4309346246"],"abstract_inverted_index":{"Radar":[0],"object":[1,92],"detection":[2,34,93,110,142],"refers":[3],"to":[4,22,100,118,123,139],"identify":[5],"objects":[6],"from":[7,62],"radar":[8,27,91],"data,":[9],"and":[10,29,194],"the":[11,18,23,33,63,68,74,90,96,120,141,148,151,159,178,182],"topic":[12],"has":[13,54],"received":[14],"increasing":[15],"interest":[16],"during":[17],"last":[19],"years,":[20],"due":[21],"appealing":[24],"property":[25,66],"of":[26,67,77,150,191],"imaging":[28],"evident":[30],"applications.":[31],"However,":[32],"performance":[35,143],"heavily":[36],"relied":[37],"on":[38,158,181],"semantic":[39],"information":[40],"extraction,":[41],"which":[42],"is":[43,112,134],"a":[44,83,107,128],"great":[45],"challenge":[46],"in":[47,115,136],"practical":[48],"settings.":[49],"Moreover,":[50,106],"although":[51],"remarkable":[52],"progress":[53],"been":[55],"made,":[56],"most":[57],"previous":[58],"attempts":[59],"are":[60,196],"restrained":[61],"essentially":[64],"limited":[65],"employed":[69],"single":[70],"modality.":[71],"Inspired":[72],"by":[73],"recent":[75],"success":[76],"cross-modality":[78,85],"deep":[79,86],"learning,":[80],"we":[81,154],"propose":[82],"novel":[84,108,129],"learning":[87],"framework":[88,170],"for":[89],"task":[94],"using":[95],"Squeeze-and-Excitation":[97],"network,":[98],"aiming":[99],"provide":[101],"more":[102],"powerful":[103],"feature":[104],"representation.":[105],"noisy":[109],"approach":[111],"also":[113],"explored":[114],"our":[116,137,169],"study,":[117],"increase":[119],"model's":[121],"ability":[122],"handle":[124],"with":[125,185],"noise.":[126],"Finally,":[127],"weighted":[130],"location":[131],"fusion":[132],"strategy":[133],"introduced":[135],"framework,":[138,153],"improve":[140],"further.":[144],"To":[145],"empirically":[146],"investigate":[147],"effectiveness":[149],"proposed":[152],"conduct":[155],"extensive":[156],"experiments":[157],"2021":[160],"ICMR":[161],"ROD":[162],"challenge.":[163],"The":[164],"obtained":[165],"results":[166],"suggest":[167],"that":[168],"outperforms":[171],"related":[172],"approaches.":[173],"Our":[174],"method":[175],"ranks":[176],"as":[177],"3rd":[179],"place":[180],"final":[183],"leaderboard,":[184],"an":[186],"average":[187],"precision":[188],"(AP)":[189],"percentage":[190],"76.1.":[192],"Models":[193],"codes":[195],"available":[197],"at":[198],"https://github.com/sunpengliang/modelConfusion.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}