{"id":"https://openalex.org/W4392903826","doi":"https://doi.org/10.1109/icassp48485.2024.10447574","title":"Reparameterization Head for Efficient Multi-Input Networks","display_name":"Reparameterization Head for Efficient Multi-Input Networks","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392903826","doi":"https://doi.org/10.1109/icassp48485.2024.10447574"},"language":"en","primary_location":{"id":"doi:10.1109/icassp48485.2024.10447574","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10447574","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 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/A5047533842","display_name":"Keke Tang","orcid":"https://orcid.org/0000-0003-0377-1022"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Keke Tang","raw_affiliation_strings":["Guangzhou University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou University","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5094178951","display_name":"Wenyu Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenyu Zhao","raw_affiliation_strings":["Guangzhou University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou University","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052138365","display_name":"Weilong Peng","orcid":"https://orcid.org/0000-0001-5820-889X"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weilong Peng","raw_affiliation_strings":["Guangzhou University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou University","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083888610","display_name":"Xiang Fang","orcid":"https://orcid.org/0000-0003-3231-5771"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Xiang Fang","raw_affiliation_strings":["Nanyang Technological University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanyang Technological University","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102014291","display_name":"Xiaodong Cui","orcid":"https://orcid.org/0000-0003-4865-1307"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaodong Cui","raw_affiliation_strings":["Northwestern Polytechnical University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038915198","display_name":"Peican Zhu","orcid":"https://orcid.org/0000-0002-8389-1093"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peican Zhu","raw_affiliation_strings":["Northwestern Polytechnical University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056608045","display_name":"Zhihong Tian","orcid":"https://orcid.org/0000-0002-9409-5359"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihong Tian","raw_affiliation_strings":["Guangzhou University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou University","institution_ids":["https://openalex.org/I37987034"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6190","last_page":"6194"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9969000220298767,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9955000281333923,"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":0.8458489775657654},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.6393251419067383},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5706250667572021},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4727362394332886},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4102631211280823},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.39283353090286255},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36589890718460083},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.3450363278388977}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8458489775657654},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.6393251419067383},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5706250667572021},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4727362394332886},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4102631211280823},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.39283353090286255},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36589890718460083},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3450363278388977},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp48485.2024.10447574","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10447574","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 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":0.4000000059604645}],"awards":[{"id":"https://openalex.org/G1177137375","display_name":null,"funder_award_id":"62102105","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2318274618","display_name":null,"funder_award_id":"2022A1515011501","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G3548982107","display_name":null,"funder_award_id":"62102105","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G4388579091","display_name":null,"funder_award_id":"2022A1515011501","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5739047148","display_name":null,"funder_award_id":"2020AAA0107704","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G5868903570","display_name":null,"funder_award_id":"2020AAA0107704","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6177238726","display_name":null,"funder_award_id":"202201020229","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6429873286","display_name":null,"funder_award_id":"202201020229","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G8075237089","display_name":null,"funder_award_id":"2022A1515010138","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G919837104","display_name":null,"funder_award_id":"2022A1515010138","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1644641054","https://openalex.org/W1686810756","https://openalex.org/W1920022804","https://openalex.org/W2097117768","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2560609797","https://openalex.org/W2567696942","https://openalex.org/W2620983607","https://openalex.org/W2962965870","https://openalex.org/W2963000224","https://openalex.org/W2981609437","https://openalex.org/W3098585582","https://openalex.org/W3167976421","https://openalex.org/W3171038842","https://openalex.org/W3194685464","https://openalex.org/W4205601925","https://openalex.org/W4290935136","https://openalex.org/W4306784332","https://openalex.org/W4312816015","https://openalex.org/W4378464458","https://openalex.org/W4382466349","https://openalex.org/W4386076083","https://openalex.org/W4386598286","https://openalex.org/W6637373629","https://openalex.org/W6640300118","https://openalex.org/W6684191040","https://openalex.org/W6725543821","https://openalex.org/W6726275242","https://openalex.org/W6738686954","https://openalex.org/W6763422710","https://openalex.org/W6773650956","https://openalex.org/W6845946312","https://openalex.org/W6853168692"],"related_works":["https://openalex.org/W2952348651","https://openalex.org/W2375742443","https://openalex.org/W2391251536","https://openalex.org/W2149381099","https://openalex.org/W4200520489","https://openalex.org/W1483190388","https://openalex.org/W2362198218","https://openalex.org/W2061536531","https://openalex.org/W1982750869","https://openalex.org/W4285818394"],"abstract_inverted_index":{"Reparameterization":[0],"techniques":[1],"have":[2],"demonstrated":[3],"their":[4,15],"efficacy":[5],"in":[6,30,89],"improving":[7],"the":[8,43,79,85,101],"efficiency":[9],"of":[10,81,87,103],"deep":[11],"neural":[12,52],"networks.":[13,53],"However,":[14],"application":[16,80],"has":[17],"been":[18],"largely":[19,33],"confined":[20],"to":[21,47,66],"single-input":[22],"network":[23],"structures,":[24],"leaving":[25],"multi-input":[26,51,71],"ones,":[27],"commonly":[28],"encountered":[29],"real-world":[31],"applications,":[32],"unexplored.":[34],"In":[35],"this":[36],"paper,":[37],"we":[38],"formulate":[39],"reparameterization":[40,49],"head":[41],"(RepHead),":[42],"first":[44],"framework":[45],"designed":[46],"introduce":[48],"into":[50,58,73],"RepHead":[54,88,104],"compresses":[55],"multiple":[56],"inputs":[57],"a":[59],"single":[60],"input":[61],"and":[62,92,109],"employs":[63],"reconstruction":[64],"operations":[65],"recover":[67],"them,":[68],"thereby":[69,77],"transforming":[70],"networks":[72],"single-input,":[74],"multibranch":[75],"architectures,":[76],"enabling":[78],"reparameterization.":[82],"We":[83],"demonstrate":[84],"usage":[86],"both":[90],"image":[91],"point":[93],"cloud":[94],"domains.":[95],"Extensive":[96],"experimental":[97],"results":[98],"validate":[99],"that":[100],"integration":[102],"substantially":[105],"reduces":[106],"computational":[107],"overhead":[108],"memory":[110],"requirements":[111],"while":[112],"maintaining":[113],"minimal":[114],"performance":[115],"loss.":[116]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
