{"id":"https://openalex.org/W3161176090","doi":"https://doi.org/10.1109/icassp39728.2021.9413767","title":"EADNet: Efficient Asymmetric Dilated Network For Semantic Segmentation","display_name":"EADNet: Efficient Asymmetric Dilated Network For Semantic Segmentation","publication_year":2021,"publication_date":"2021-05-13","ids":{"openalex":"https://openalex.org/W3161176090","doi":"https://doi.org/10.1109/icassp39728.2021.9413767","mag":"3161176090"},"language":"en","primary_location":{"id":"doi:10.1109/icassp39728.2021.9413767","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9413767","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5018553178","display_name":"Qihang Yang","orcid":"https://orcid.org/0000-0001-6147-4531"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qihang Yang","raw_affiliation_strings":["School of Information Science and Technology, Fudan University"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075177714","display_name":"Tao Chen","orcid":"https://orcid.org/0000-0001-8239-1698"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Chen","raw_affiliation_strings":["School of Information Science and Technology, Fudan University"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102013595","display_name":"Jiayuan Fan","orcid":"https://orcid.org/0000-0001-7494-0255"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiayuan Fan","raw_affiliation_strings":["Academy for Engineering and Technology, Fudan University"],"affiliations":[{"raw_affiliation_string":"Academy for Engineering and Technology, Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023557586","display_name":"Ye Lu","orcid":"https://orcid.org/0000-0003-0805-6394"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ye Lu","raw_affiliation_strings":["School of Information Science and Technology, Fudan University"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028484957","display_name":"Chongyan Zuo","orcid":null},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chongyan Zuo","raw_affiliation_strings":["Shanghai Huawei Technologies Co., Ltd, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Huawei Technologies Co., Ltd, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033065013","display_name":"Qinghua Chi","orcid":"https://orcid.org/0000-0003-3159-6583"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinghua Chi","raw_affiliation_strings":["Shanghai Huawei Technologies Co., Ltd, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Huawei Technologies Co., Ltd, China","institution_ids":["https://openalex.org/I2250955327"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5018553178"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":1.0567,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.78617647,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2315","last_page":"2319"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9983000159263611,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.8351697325706482},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7664318084716797},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6935293078422546},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6345563530921936},{"id":"https://openalex.org/keywords/dilation","display_name":"Dilation (metric space)","score":0.62581467628479},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6160068511962891},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5609543323516846},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.4970259964466095},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.45373812317848206},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4429607391357422},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4331640601158142},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41970208287239075},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4117942154407501},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11782842874526978}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8351697325706482},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7664318084716797},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6935293078422546},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6345563530921936},{"id":"https://openalex.org/C2780757906","wikidata":"https://www.wikidata.org/wiki/Q5276676","display_name":"Dilation (metric space)","level":2,"score":0.62581467628479},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6160068511962891},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5609543323516846},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.4970259964466095},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.45373812317848206},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4429607391357422},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4331640601158142},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41970208287239075},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4117942154407501},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11782842874526978},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp39728.2021.9413767","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9413767","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7599999904632568}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1522301498","https://openalex.org/W1677182931","https://openalex.org/W1836465849","https://openalex.org/W1901129140","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2340897893","https://openalex.org/W2412782625","https://openalex.org/W2419448466","https://openalex.org/W2560023338","https://openalex.org/W2563705555","https://openalex.org/W2611259176","https://openalex.org/W2762439315","https://openalex.org/W2886934227","https://openalex.org/W2890782586","https://openalex.org/W2902709614","https://openalex.org/W2907965334","https://openalex.org/W2949117887","https://openalex.org/W2953139137","https://openalex.org/W2962772649","https://openalex.org/W2963418739","https://openalex.org/W2963881378","https://openalex.org/W2963890956","https://openalex.org/W2964217532","https://openalex.org/W2990875140","https://openalex.org/W3035414587","https://openalex.org/W4293406525","https://openalex.org/W6620707391","https://openalex.org/W6631190155","https://openalex.org/W6638667902","https://openalex.org/W6715287400","https://openalex.org/W6717372056","https://openalex.org/W6737324727","https://openalex.org/W6754879843","https://openalex.org/W6757256307","https://openalex.org/W6757855356","https://openalex.org/W6770473083"],"related_works":["https://openalex.org/W2964954556","https://openalex.org/W3019910406","https://openalex.org/W4287548622","https://openalex.org/W3117849209","https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W3173347409","https://openalex.org/W3144569342","https://openalex.org/W4313052709","https://openalex.org/W2945274617"],"abstract_inverted_index":{"Due":[0],"to":[1,18,25,60,87],"real-time":[2],"image":[3],"semantic":[4,21,43,117],"segmentation":[5,22,44,103,118],"needs":[6],"on":[7,93],"power":[8],"constrained":[9],"edge":[10],"devices,":[11],"there":[12],"has":[13],"been":[14],"an":[15,39,69],"increasing":[16],"desire":[17],"design":[19],"lightweight":[20,116],"neural":[23],"network,":[24,45],"simultaneously":[26],"reduce":[27],"computational":[28],"cost":[29],"and":[30,65],"increase":[31],"inference":[32],"speed.":[33],"In":[34],"this":[35],"paper,":[36],"we":[37],"propose":[38],"efficient":[40],"asymmetric":[41,53],"dilated":[42],"named":[46],"EADNet,":[47],"which":[48],"consists":[49],"of":[50,68,105,110],"multiple":[51],"developed":[52],"convolution":[54,77],"branches":[55],"with":[56,80,107],"different":[57],"dilation":[58],"rates":[59],"capture":[61,88],"the":[62,94],"variable":[63],"shapes":[64],"scales":[66],"information":[67],"image.":[70],"Specially,":[71],"a":[72,82],"multi-scale":[73],"multi-shape":[74],"receptive":[75],"field":[76],"(MMRFC)":[78],"block":[79],"only":[81],"few":[83],"parameters":[84,111],"is":[85],"designed":[86],"such":[89],"information.":[90],"Experimental":[91],"results":[92],"Cityscapes":[95],"dataset":[96],"demonstrate":[97],"that":[98],"our":[99],"proposed":[100],"EADNet":[101],"achieves":[102],"mIoU":[104],"67.1%":[106],"smallest":[108],"number":[109],"(only":[112],"0.35M)":[113],"among":[114],"mainstream":[115],"networks.":[119]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1}],"updated_date":"2026-02-15T05:58:04.055770","created_date":"2025-10-10T00:00:00"}
