{"id":"https://openalex.org/W2948751033","doi":"https://doi.org/10.1109/tpami.2020.3041871","title":"DiCENet: Dimension-Wise Convolutions for Efficient Networks","display_name":"DiCENet: Dimension-Wise Convolutions for Efficient Networks","publication_year":2020,"publication_date":"2020-12-02","ids":{"openalex":"https://openalex.org/W2948751033","doi":"https://doi.org/10.1109/tpami.2020.3041871","mag":"2948751033","pmid":"https://pubmed.ncbi.nlm.nih.gov/33264092"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2020.3041871","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2020.3041871","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1906.03516","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074132108","display_name":"Sachin Mehta","orcid":"https://orcid.org/0000-0002-5420-4725"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sachin Mehta","raw_affiliation_strings":["University of Washington, Seattle, WA, USA","Department of Electrical and Computer Engineering, University of Washington, 7284 Seattle, Washington, United States, (e-mail: sacmehta@uw.edu)"],"raw_orcid":"https://orcid.org/0000-0002-5420-4725","affiliations":[{"raw_affiliation_string":"University of Washington, Seattle, WA, USA","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Washington, 7284 Seattle, Washington, United States, (e-mail: sacmehta@uw.edu)","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082305994","display_name":"Hannaneh Hajishirzi","orcid":"https://orcid.org/0000-0002-1055-6657"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hannaneh Hajishirzi","raw_affiliation_strings":["University of Washington, Seattle, WA, USA","Allen School for Computer Science and Engineering, University of Washington, 7284 Seattle, Washington, United States, (e-mail: hannaneh@washington.edu)"],"raw_orcid":"https://orcid.org/0000-0002-1055-6657","affiliations":[{"raw_affiliation_string":"University of Washington, Seattle, WA, USA","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"Allen School for Computer Science and Engineering, University of Washington, 7284 Seattle, Washington, United States, (e-mail: hannaneh@washington.edu)","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056246621","display_name":"Mohammad Rastegari","orcid":"https://orcid.org/0000-0001-9606-3687"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohammad Rastegari","raw_affiliation_strings":["University of Washington, Seattle, WA, USA","University of Washington ;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Washington, Seattle, WA, USA","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"University of Washington ;","institution_ids":["https://openalex.org/I201448701"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I201448701"],"apc_list":null,"apc_paid":null,"fwci":0.1929,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.4633763,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"44","issue":"5","first_page":"2416","last_page":"2425"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9993000030517578,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9987000226974487,"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/dice","display_name":"Dice","score":0.8583431839942932},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.7425984144210815},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7411834597587585},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5954521298408508},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5907831192016602},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.589677095413208},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5171586871147156},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.48331236839294434},{"id":"https://openalex.org/keywords/separable-space","display_name":"Separable space","score":0.4665999412536621},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.46274900436401367},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4179469048976898},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.35284721851348877},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.30528151988983154},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14560681581497192},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.1041727066040039}],"concepts":[{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.8583431839942932},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.7425984144210815},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7411834597587585},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5954521298408508},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5907831192016602},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.589677095413208},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5171586871147156},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.48331236839294434},{"id":"https://openalex.org/C70710897","wikidata":"https://www.wikidata.org/wiki/Q680081","display_name":"Separable space","level":2,"score":0.4665999412536621},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.46274900436401367},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4179469048976898},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.35284721851348877},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30528151988983154},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14560681581497192},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.1041727066040039},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":5,"locations":[{"id":"doi:10.1109/tpami.2020.3041871","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2020.3041871","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:33264092","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33264092","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null},{"id":"pmh:oai:arXiv.org:1906.03516","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1906.03516","pdf_url":"https://arxiv.org/pdf/1906.03516","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:2948751033","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/1906.03516","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1906.03516","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1906.03516","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1906.03516","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1906.03516","pdf_url":"https://arxiv.org/pdf/1906.03516","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3812534482","display_name":null,"funder_award_id":"N00014-18-1-2826","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G4325372433","display_name":"RI: Small: Learning to Read, Ground, and Reason in Multimodal Text","funder_award_id":"1616112","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6497436218","display_name":"CAREER: Learning Scalable Models for Grounded Semantic Parsing","funder_award_id":"1252835","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6859523949","display_name":null,"funder_award_id":"N66001-19-2-403","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"},{"id":"https://openalex.org/G8215844777","display_name":null,"funder_award_id":"N66001-19","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"},{"id":"https://openalex.org/G8876996369","display_name":null,"funder_award_id":"N00014","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309327","display_name":"Google","ror":"https://ror.org/00njsd438"},{"id":"https://openalex.org/F4320310094","display_name":"University of Washington","ror":"https://ror.org/00cvxb145"},{"id":"https://openalex.org/F4320317052","display_name":"Allen Institute for Artificial Intelligence","ror":"https://ror.org/05w520734"},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"},{"id":"https://openalex.org/F4320332195","display_name":"Samsung","ror":"https://ror.org/04w3jy968"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2948751033.pdf","grobid_xml":"https://content.openalex.org/works/W2948751033.grobid-xml"},"referenced_works_count":90,"referenced_works":["https://openalex.org/W753847829","https://openalex.org/W1650736245","https://openalex.org/W1677182931","https://openalex.org/W1821462560","https://openalex.org/W1861492603","https://openalex.org/W1903029394","https://openalex.org/W2031489346","https://openalex.org/W2037227137","https://openalex.org/W2097117768","https://openalex.org/W2117539524","https://openalex.org/W2144794286","https://openalex.org/W2154579312","https://openalex.org/W2161758346","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2233116163","https://openalex.org/W2253986341","https://openalex.org/W2267635276","https://openalex.org/W2300242332","https://openalex.org/W2319920447","https://openalex.org/W2412782625","https://openalex.org/W2419448466","https://openalex.org/W2469490737","https://openalex.org/W2531409750","https://openalex.org/W2549139847","https://openalex.org/W2560023338","https://openalex.org/W2570343428","https://openalex.org/W2599765304","https://openalex.org/W2612445135","https://openalex.org/W2630837129","https://openalex.org/W2739879705","https://openalex.org/W2752782242","https://openalex.org/W2782417188","https://openalex.org/W2790381683","https://openalex.org/W2796502408","https://openalex.org/W2803097213","https://openalex.org/W2883780447","https://openalex.org/W2884367402","https://openalex.org/W2884751099","https://openalex.org/W2886851211","https://openalex.org/W2894936553","https://openalex.org/W2902251695","https://openalex.org/W2904699287","https://openalex.org/W2929125140","https://openalex.org/W2944779197","https://openalex.org/W2949117887","https://openalex.org/W2949939395","https://openalex.org/W2950894517","https://openalex.org/W2951548327","https://openalex.org/W2953106684","https://openalex.org/W2953139137","https://openalex.org/W2955425717","https://openalex.org/W2962772649","https://openalex.org/W2962835968","https://openalex.org/W2963000224","https://openalex.org/W2963122961","https://openalex.org/W2963125010","https://openalex.org/W2963145956","https://openalex.org/W2963163009","https://openalex.org/W2963374479","https://openalex.org/W2963418739","https://openalex.org/W2963420686","https://openalex.org/W2963446712","https://openalex.org/W2963674932","https://openalex.org/W2963697527","https://openalex.org/W2963821229","https://openalex.org/W2963881378","https://openalex.org/W2963918968","https://openalex.org/W2963953305","https://openalex.org/W2963993763","https://openalex.org/W2964081807","https://openalex.org/W2964118293","https://openalex.org/W2964217532","https://openalex.org/W2964299589","https://openalex.org/W2967733054","https://openalex.org/W2970967090","https://openalex.org/W2982083293","https://openalex.org/W2982479999","https://openalex.org/W3022419825","https://openalex.org/W3106250896","https://openalex.org/W6637078681","https://openalex.org/W6637373629","https://openalex.org/W6638523607","https://openalex.org/W6677580257","https://openalex.org/W6684191040","https://openalex.org/W6693397755","https://openalex.org/W6729956949","https://openalex.org/W6737664043","https://openalex.org/W6763050415","https://openalex.org/W6766029800"],"related_works":["https://openalex.org/W3194572148","https://openalex.org/W2807092842","https://openalex.org/W3041249960","https://openalex.org/W3007268491","https://openalex.org/W3177454358","https://openalex.org/W3148636574","https://openalex.org/W3020644674","https://openalex.org/W3175104294","https://openalex.org/W3180561896","https://openalex.org/W3086229892","https://openalex.org/W3084106352","https://openalex.org/W3202232410","https://openalex.org/W2788188949","https://openalex.org/W2755187316","https://openalex.org/W3007952981","https://openalex.org/W2617957099","https://openalex.org/W2911925209","https://openalex.org/W1928419358","https://openalex.org/W2964342346","https://openalex.org/W3045564827"],"abstract_inverted_index":{"We":[0],"introduce":[1],"a":[2],"novel":[3],"and":[4,16,49,62,74,117,138,170],"generic":[5],"convolutional":[6,24],"unit,":[7,9],"DiCE":[8,43,58,82,91],"that":[10,149],"is":[11,60],"built":[12],"using":[13],"dimension-wise":[14,17,20,34,39],"convolutions":[15,21],"fusion.":[18],"The":[19,57],"apply":[22],"light-weight":[23],"filtering":[25],"across":[26,87,107],"each":[27],"dimension":[28],"of":[29],"the":[30,42,54,81,97,121,124],"input":[31,55],"tensor":[32],"while":[33],"fusion":[35],"efficiently":[36,46],"combines":[37],"these":[38],"representations;":[40],"allowing":[41],"unit":[44,59,83],"to":[45,70,77,95,144,158],"encode":[47],"spatial":[48],"channel-wise":[50],"information":[51],"contained":[52],"in":[53,153,156],"tensor.":[56],"simple":[61],"can":[63],"be":[64],"seamlessly":[65],"integrated":[66],"with":[67],"any":[68],"architecture":[69],"improve":[71],"its":[72],"efficiency":[73],"performance.":[75],"Compared":[76],"depth-wise":[78],"separable":[79,160],"convolutions,":[80],"shows":[84],"significant":[85,102],"improvements":[86,103],"different":[88],"architectures.":[89],"When":[90],"units":[92],"are":[93,150],"stacked":[94],"build":[96],"DiCENet":[98,125,141],"model,":[99],"we":[100],"observe":[101],"over":[104],"state-of-the-art":[105,132,159],"models":[106,135],"various":[108],"computer":[109],"vision":[110],"tasks":[111,145],"including":[112,164],"image":[113],"classification,":[114],"object":[115,147],"detection,":[116],"semantic":[118],"segmentation.":[119],"On":[120],"ImageNet":[122],"dataset,":[123],"delivers":[126],"2-4":[127],"percent":[128],"higher":[129],"accuracy":[130],"than":[131],"manually":[133],"designed":[134],"(e.g.,":[136,146,168],"MobileNetv2":[137],"ShuffleNetv2).":[139],"Also,":[140],"generalizes":[142],"better":[143],"detection)":[148],"often":[151],"used":[152],"resource-constrained":[154],"devices":[155],"comparison":[157],"convolution-based":[161],"efficient":[162],"networks,":[163],"neural":[165],"search-based":[166],"methods":[167],"MobileNetv3":[169],"MixNet).":[171]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2025-10-10T00:00:00"}
