{"id":"https://openalex.org/W7160326369","doi":"https://doi.org/10.1109/wacv61042.2026.00253","title":"Shift-Equivariant Complex-Valued Convolutional Neural Networks","display_name":"Shift-Equivariant Complex-Valued Convolutional Neural Networks","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7160326369","doi":"https://doi.org/10.1109/wacv61042.2026.00253"},"language":null,"primary_location":{"id":"doi:10.1109/wacv61042.2026.00253","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00253","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","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/A5003637683","display_name":"Quentin Gabot","orcid":null},"institutions":[{"id":"https://openalex.org/I4210107720","display_name":"CentraleSup\u00e9lec","ror":"https://ror.org/019tcpt25","country_code":"FR","type":"facility","lineage":["https://openalex.org/I277688954","https://openalex.org/I4210107720"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Quentin Gabot","raw_affiliation_strings":["Universit&#x00E9; Paris-Saclay,SONDRA, CentraleSup&#x00E9;lec,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit&#x00E9; Paris-Saclay,SONDRA, CentraleSup&#x00E9;lec,France","institution_ids":["https://openalex.org/I4210107720"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041754244","display_name":"Teck-Yian Lim","orcid":"https://orcid.org/0000-0002-8121-8137"},"institutions":[{"id":"https://openalex.org/I28490864","display_name":"DSO National Laboratories","ror":"https://ror.org/03e05fb06","country_code":"SG","type":"nonprofit","lineage":["https://openalex.org/I28490864"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Teck-Yian Lim","raw_affiliation_strings":["DSO National Laboratories,Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DSO National Laboratories,Singapore","institution_ids":["https://openalex.org/I28490864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132819877","display_name":"J\u00e9r\u00e9my Fix","orcid":null},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210107720","display_name":"CentraleSup\u00e9lec","ror":"https://ror.org/019tcpt25","country_code":"FR","type":"facility","lineage":["https://openalex.org/I277688954","https://openalex.org/I4210107720"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"J\u00e9r\u00e9my Fix","raw_affiliation_strings":["Universit&#x00E9; Paris-Saclay,LORIA, CNRS, CentraleSup&#x00E9;lec,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit&#x00E9; Paris-Saclay,LORIA, CNRS, CentraleSup&#x00E9;lec,France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I4210107720"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001036980","display_name":"Joana Frontera\u2013Pons","orcid":"https://orcid.org/0000-0003-0438-5801"},"institutions":[{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Joana Frontera-Pons","raw_affiliation_strings":["Universit&#x00E9; Paris-Saclay,DEMR, ONERA,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit&#x00E9; Paris-Saclay,DEMR, ONERA,France","institution_ids":["https://openalex.org/I277688954"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029566720","display_name":"Chengfang Ren","orcid":"https://orcid.org/0000-0001-8438-4539"},"institutions":[{"id":"https://openalex.org/I4210107720","display_name":"CentraleSup\u00e9lec","ror":"https://ror.org/019tcpt25","country_code":"FR","type":"facility","lineage":["https://openalex.org/I277688954","https://openalex.org/I4210107720"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Chengfang Ren","raw_affiliation_strings":["Universit&#x00E9; Paris-Saclay,SONDRA, CentraleSup&#x00E9;lec,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit&#x00E9; Paris-Saclay,SONDRA, CentraleSup&#x00E9;lec,France","institution_ids":["https://openalex.org/I4210107720"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022689387","display_name":"Jean\u2010Philippe Ovarlez","orcid":"https://orcid.org/0000-0001-8056-4196"},"institutions":[{"id":"https://openalex.org/I4210107720","display_name":"CentraleSup\u00e9lec","ror":"https://ror.org/019tcpt25","country_code":"FR","type":"facility","lineage":["https://openalex.org/I277688954","https://openalex.org/I4210107720"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Jean-Philippe Ovarlez","raw_affiliation_strings":["Universit&#x00E9; Paris-Saclay,SONDRA, CentraleSup&#x00E9;lec,France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit&#x00E9; Paris-Saclay,SONDRA, CentraleSup&#x00E9;lec,France","institution_ids":["https://openalex.org/I4210107720"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"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":"2575","last_page":"2584"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.3930000066757202,"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"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.3930000066757202,"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/T12676","display_name":"Machine Learning and ELM","score":0.10589999705553055,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.07670000195503235,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4490000009536743},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41040000319480896},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3865000009536743},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.33070001006126404},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.26460000872612}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6068000197410583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5810999870300293},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4490000009536743},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41040000319480896},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3865000009536743},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.33070001006126404},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3131999969482422},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.26460000872612},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.24809999763965607},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.24480000138282776}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv61042.2026.00253","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00253","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W658512522","https://openalex.org/W1550596064","https://openalex.org/W1880836256","https://openalex.org/W1901129140","https://openalex.org/W1917898038","https://openalex.org/W1992350595","https://openalex.org/W2021814280","https://openalex.org/W2036043111","https://openalex.org/W2060834220","https://openalex.org/W2078985447","https://openalex.org/W2117472121","https://openalex.org/W2133989913","https://openalex.org/W2143572124","https://openalex.org/W2149148820","https://openalex.org/W2194775991","https://openalex.org/W2558748708","https://openalex.org/W2836501717","https://openalex.org/W2909308970","https://openalex.org/W2963351448","https://openalex.org/W3126857817","https://openalex.org/W3137984567","https://openalex.org/W3160986733","https://openalex.org/W3175122022","https://openalex.org/W4237764598","https://openalex.org/W4288438322","https://openalex.org/W4289654500","https://openalex.org/W4312427063","https://openalex.org/W4390874128","https://openalex.org/W4394029171","https://openalex.org/W4402727348","https://openalex.org/W4404531679","https://openalex.org/W7133190532","https://openalex.org/W7133210076","https://openalex.org/W7133237198"],"related_works":[],"abstract_inverted_index":{"Convolutional":[0],"neural":[1,19,95,108],"networks":[2,109],"have":[3],"shown":[4],"remarkable":[5],"performance":[6],"in":[7,149,156],"recent":[8],"years":[9],"on":[10,104,138],"various":[11],"computer":[12,140],"vision":[13,141],"problems.":[14],"However,":[15],"the":[16,42,45,77,102,130,146,153],"traditional":[17],"convolutional":[18],"network":[20],"architecture":[21],"lacks":[22],"a":[23,49,112,117,122],"critical":[24],"property:":[25],"shift":[26,80,85],"equivariance":[27,86,154],"and":[28,33,51,62,115,159],"invariance,":[29,81],"broken":[30],"by":[31,59,70],"downsampling":[32,61],"upsampling":[34,63],"operations.":[35],"Although":[36],"data":[37],"augmentation":[38],"techniques":[39],"can":[40],"help":[41],"model":[43],"learn":[44],"latter":[46],"property":[47,148,155],"empirically,":[48],"consistent":[50],"systematic":[52],"way":[53],"to":[54,84,93,106,127],"achieve":[55],"this":[56,98,136],"goal":[57],"is":[58],"designing":[60],"layers":[64],"that":[65],"theoretically":[66],"guarantee":[67],"these":[68],"properties":[69],"construction.":[71],"Adaptive":[72],"Polyphase":[73,89],"Sampling":[74],"(APS)":[75],"introduced":[76],"cornerstone":[78],"for":[79,144],"later":[82],"extended":[83],"with":[87,116],"Learnable":[88],"up/downsampling":[90],"(LPS)":[91],"applied":[92],"real-valued":[94],"networks.":[96],"In":[97],"paper,":[99],"we":[100],"extend":[101],"work":[103],"LPS":[105],"complex-valued":[107],"both":[110,157],"from":[111,125],"theoretical":[113],"perspective":[114],"novel":[118],"building":[119],"block":[120],"of":[121],"projection":[123],"layer":[124],"C":[126],"R":[128],"before":[129],"Gumbel":[131],"Soft-max.":[132],"We":[133],"finally":[134],"evaluate":[135],"extension":[137],"several":[139],"problems,":[142,162],"specifically":[143],"either":[145],"invariance":[147],"classification":[150],"tasks":[151],"or":[152],"reconstruction":[158],"semantic":[160],"segmentation":[161],"using":[163],"polarimetric":[164],"Synthetic":[165],"Aperture":[166],"Radar":[167],"images.":[168]},"counts_by_year":[],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2026-05-06T00:00:00"}
