{"id":"https://openalex.org/W4388593456","doi":"https://doi.org/10.1145/3625343.3625358","title":"MGAM: Mixed-Gaussian Attention Module for Convolutional Neural Networks","display_name":"MGAM: Mixed-Gaussian Attention Module for Convolutional Neural Networks","publication_year":2023,"publication_date":"2023-09-15","ids":{"openalex":"https://openalex.org/W4388593456","doi":"https://doi.org/10.1145/3625343.3625358"},"language":"en","primary_location":{"id":"doi:10.1145/3625343.3625358","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1145/3625343.3625358","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 Asia Conference on Artificial Intelligence, Machine Learning and Robotics","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/A5043600365","display_name":"Yanzi Li","orcid":null},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanzi Li","raw_affiliation_strings":["College of Electronics and Information Engineering, Shenzhen University, China"],"raw_orcid":"https://orcid.org/0009-0001-1013-5035","affiliations":[{"raw_affiliation_string":"College of Electronics and Information Engineering, Shenzhen University, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007829731","display_name":"Huoxiang Yang","orcid":"https://orcid.org/0000-0002-0388-6623"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huoxiang Yang","raw_affiliation_strings":["College of Electronics and Information Engineering, Shenzhen University, China"],"raw_orcid":"https://orcid.org/0000-0002-0388-6623","affiliations":[{"raw_affiliation_string":"College of Electronics and Information Engineering, Shenzhen University, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017906749","display_name":"Qingyu Mao","orcid":"https://orcid.org/0000-0003-2473-4976"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingyu Mao","raw_affiliation_strings":["College of Electronics and Information Engineering, Shenzhen University, China"],"raw_orcid":"https://orcid.org/0000-0003-2473-4976","affiliations":[{"raw_affiliation_string":"College of Electronics and Information Engineering, Shenzhen University, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038040039","display_name":"Fanyang Meng","orcid":"https://orcid.org/0000-0001-5725-2178"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fanyang Meng","raw_affiliation_strings":["Research Center of Networks and Communication, Peng Cheng Laboratory, China"],"raw_orcid":"https://orcid.org/0000-0001-5725-2178","affiliations":[{"raw_affiliation_string":"Research Center of Networks and Communication, Peng Cheng Laboratory, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100657559","display_name":"Yongsheng Liang","orcid":"https://orcid.org/0000-0002-0891-5577"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongsheng Liang","raw_affiliation_strings":["College of Electronics and Information Engineering, Shenzhen University, China"],"raw_orcid":"https://orcid.org/0000-0002-0891-5577","affiliations":[{"raw_affiliation_string":"College of Electronics and Information Engineering, Shenzhen University, China","institution_ids":["https://openalex.org/I180726961"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1123,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.42444853,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9997000098228455,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.996999979019165,"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/discriminative-model","display_name":"Discriminative model","score":0.7969931364059448},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7932695746421814},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7460817098617554},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.721840500831604},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.6228265762329102},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.563883900642395},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.537699282169342},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.4672413170337677},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.440645307302475},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3745819926261902},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3375384509563446},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13974180817604065},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.10103482007980347},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07678329944610596},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.060180842876434326}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7969931364059448},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7932695746421814},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7460817098617554},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.721840500831604},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6228265762329102},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.563883900642395},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.537699282169342},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.4672413170337677},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.440645307302475},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3745819926261902},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3375384509563446},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13974180817604065},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.10103482007980347},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07678329944610596},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.060180842876434326},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3625343.3625358","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1145/3625343.3625358","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 Asia Conference on Artificial Intelligence, Machine Learning and Robotics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7900000214576721,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2117539524","https://openalex.org/W2128272608","https://openalex.org/W2194775991","https://openalex.org/W2559655401","https://openalex.org/W2613718673","https://openalex.org/W2618530766","https://openalex.org/W2752782242","https://openalex.org/W2884585870","https://openalex.org/W2898732869","https://openalex.org/W2962858109","https://openalex.org/W2963150697","https://openalex.org/W2963163009","https://openalex.org/W2963495494","https://openalex.org/W2999905431","https://openalex.org/W3008836694","https://openalex.org/W3034552520","https://openalex.org/W3118608800","https://openalex.org/W3166716987","https://openalex.org/W4214666412","https://openalex.org/W4297810817","https://openalex.org/W4353007359","https://openalex.org/W6753038380"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W2378211422","https://openalex.org/W1482209366","https://openalex.org/W2110523656","https://openalex.org/W2521627374"],"abstract_inverted_index":{"Abstract\u2014Spatial":[0],"attention":[1,24,35,43,68],"holds":[2],"great":[3],"potential":[4],"for":[5],"enhancing":[6],"the":[7,30,46,77,84,104,112],"performance":[8],"of":[9,32,48,86],"convolutional":[10,39],"neural":[11],"networks":[12],"(CNNs).":[13],"This":[14],"technique":[15],"aims":[16],"to":[17,41],"select":[18],"important":[19],"spatial":[20,27,34,49,67],"regions":[21],"by":[22],"generating":[23],"masks":[25,69],"across":[26],"domains.":[28],"However,":[29],"majority":[31],"existing":[33],"works":[36],"rely":[37],"on":[38,97,103,111],"blocks":[40],"compute":[42],"weights,":[44],"disregarding":[45],"modeling":[47],"key":[50],"positions.":[51],"To":[52],"address":[53],"this":[54,56],"limitation,":[55],"paper":[57],"proposes":[58],"a":[59,100,108],"Mixed-Gaussian":[60],"Attention":[61],"Module":[62],"(MGAM).":[63],"The":[64],"MGAM":[65],"generates":[66],"that":[70],"follow":[71],"mixed":[72],"Gaussian":[73],"distribution,":[74],"effectively":[75],"highlighting":[76],"most":[78],"discriminative":[79],"semantic":[80],"features":[81],"and":[82,107],"minimizing":[83],"influence":[85],"background":[87],"noise":[88],"while":[89],"maintaining":[90],"low":[91],"model":[92],"complexity.":[93],"Our":[94],"method":[95],"based":[96],"ResNet50":[98],"achieves":[99],"1.34%":[101],"improvement":[102,110],"CIFAR100":[105],"benchmark":[106],"1%":[109],"ImageNet-200":[113],"benchmark.":[114]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
