{"id":"https://openalex.org/W4285242672","doi":"https://doi.org/10.1109/tcsvt.2022.3180274","title":"MoADNet: Mobile Asymmetric Dual-Stream Networks for Real-Time and Lightweight RGB-D Salient Object Detection","display_name":"MoADNet: Mobile Asymmetric Dual-Stream Networks for Real-Time and Lightweight RGB-D Salient Object Detection","publication_year":2022,"publication_date":"2022-06-06","ids":{"openalex":"https://openalex.org/W4285242672","doi":"https://doi.org/10.1109/tcsvt.2022.3180274"},"language":"en","primary_location":{"id":"doi:10.1109/tcsvt.2022.3180274","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2022.3180274","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Circuits and Systems for Video Technology","raw_type":"journal-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/A5035840579","display_name":"Xiao Jin","orcid":"https://orcid.org/0000-0002-5130-9505"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Jin","raw_affiliation_strings":["College of Artificial Intelligence, Nankai University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-5130-9505","affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019835364","display_name":"Kang Yi","orcid":"https://orcid.org/0000-0001-9735-1066"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kang Yi","raw_affiliation_strings":["College of Artificial Intelligence, Nankai University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042066408","display_name":"Jing Xu","orcid":"https://orcid.org/0000-0001-8532-2241"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Xu","raw_affiliation_strings":["College of Artificial Intelligence, Nankai University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0001-8532-2241","affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I205237279"],"apc_list":null,"apc_paid":null,"fwci":8.9104,"has_fulltext":false,"cited_by_count":134,"citation_normalized_percentile":{"value":0.98780952,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"32","issue":"11","first_page":"7632","last_page":"7645"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","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/T11605","display_name":"Visual Attention and Saliency Detection","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/T10036","display_name":"Advanced Neural Network Applications","score":0.9933000206947327,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.9897000193595886,"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/rgb-color-model","display_name":"RGB color model","score":0.7869040966033936},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7086219191551208},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5823913216590881},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.5086556077003479},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4976232349872589},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4885047674179077},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.41800373792648315},{"id":"https://openalex.org/keywords/asynchronous-communication","display_name":"Asynchronous communication","score":0.41104668378829956},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.11890962719917297},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.09488338232040405}],"concepts":[{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.7869040966033936},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7086219191551208},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5823913216590881},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.5086556077003479},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4976232349872589},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4885047674179077},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.41800373792648315},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.41104668378829956},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.11890962719917297},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.09488338232040405},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcsvt.2022.3180274","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2022.3180274","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Circuits and Systems for Video Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.47999998927116394,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G3830000997","display_name":null,"funder_award_id":"21JCYBJC00110","funder_id":"https://openalex.org/F4320323993","funder_display_name":"Natural Science Foundation of Tianjin City"},{"id":"https://openalex.org/G5849421052","display_name":null,"funder_award_id":"63211116","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G8559640209","display_name":null,"funder_award_id":"19JCQNJC00300","funder_id":"https://openalex.org/F4320323993","funder_display_name":"Natural Science Foundation of Tianjin City"},{"id":"https://openalex.org/G8720975476","display_name":null,"funder_award_id":"63201192","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320323993","display_name":"Natural Science Foundation of Tianjin City","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":86,"referenced_works":["https://openalex.org/W20683899","https://openalex.org/W1522301498","https://openalex.org/W1580389772","https://openalex.org/W1686810756","https://openalex.org/W1772076007","https://openalex.org/W1976409045","https://openalex.org/W1993713494","https://openalex.org/W2039298799","https://openalex.org/W2097971165","https://openalex.org/W2100470808","https://openalex.org/W2108598243","https://openalex.org/W2132083787","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2279098554","https://openalex.org/W2412782625","https://openalex.org/W2520640394","https://openalex.org/W2531409750","https://openalex.org/W2752782242","https://openalex.org/W2765838470","https://openalex.org/W2780861787","https://openalex.org/W2792965491","https://openalex.org/W2793668851","https://openalex.org/W2798807298","https://openalex.org/W2798857366","https://openalex.org/W2883780447","https://openalex.org/W2887522866","https://openalex.org/W2909381593","https://openalex.org/W2921653116","https://openalex.org/W2938260698","https://openalex.org/W2948300571","https://openalex.org/W2948500402","https://openalex.org/W2955060956","https://openalex.org/W2957414648","https://openalex.org/W2961348656","https://openalex.org/W2962159375","https://openalex.org/W2963163009","https://openalex.org/W2963529609","https://openalex.org/W2969377765","https://openalex.org/W2982083293","https://openalex.org/W2990984982","https://openalex.org/W2999458807","https://openalex.org/W3002301267","https://openalex.org/W3006465601","https://openalex.org/W3010616503","https://openalex.org/W3021371335","https://openalex.org/W3034320133","https://openalex.org/W3035284915","https://openalex.org/W3035357085","https://openalex.org/W3035633116","https://openalex.org/W3035687312","https://openalex.org/W3039991645","https://openalex.org/W3045052737","https://openalex.org/W3047800102","https://openalex.org/W3048216881","https://openalex.org/W3086388316","https://openalex.org/W3092630514","https://openalex.org/W3097725659","https://openalex.org/W3101839051","https://openalex.org/W3106587394","https://openalex.org/W3108421143","https://openalex.org/W3112885960","https://openalex.org/W3114152269","https://openalex.org/W3114848016","https://openalex.org/W3115654959","https://openalex.org/W3118710621","https://openalex.org/W3122111862","https://openalex.org/W3126725132","https://openalex.org/W3135874576","https://openalex.org/W3136838953","https://openalex.org/W3158901828","https://openalex.org/W3162418282","https://openalex.org/W3164802490","https://openalex.org/W3169865585","https://openalex.org/W3177004386","https://openalex.org/W3193915232","https://openalex.org/W3207668590","https://openalex.org/W3212645988","https://openalex.org/W4205971281","https://openalex.org/W4206947033","https://openalex.org/W4221138999","https://openalex.org/W4297775537","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6695314431","https://openalex.org/W6737664043"],"related_works":["https://openalex.org/W2595172197","https://openalex.org/W2084856301","https://openalex.org/W2127970246","https://openalex.org/W2885125400","https://openalex.org/W1989889224","https://openalex.org/W4382618745","https://openalex.org/W1973775000","https://openalex.org/W2748922771","https://openalex.org/W1987128138","https://openalex.org/W2743976221"],"abstract_inverted_index":{"RGB-D":[0,29,61],"Salient":[1],"Object":[2],"Detection":[3],"(RGB-D":[4],"SOD)":[5],"aims":[6],"at":[7],"detecting":[8],"remarkable":[9],"objects":[10],"by":[11,65,82,146,189],"complementary":[12],"information":[13,121],"from":[14],"RGB":[15,70,86],"images":[16],"and":[17,59,71,200],"depth":[18,72,77],"cues.":[19],"Although":[20],"many":[21],"outstanding":[22],"prior":[23],"arts":[24],"have":[25],"been":[26],"proposed":[27,183],"for":[28,57,119],"SOD,":[30],"most":[31],"of":[32],"them":[33],"focus":[34],"on":[35,44,95,171,215],"performance":[36,145],"enhancement,":[37],"while":[38,142],"lacking":[39],"concern":[40],"about":[41],"practical":[42],"deployment":[43],"mobile":[45,52],"devices.":[46],"In":[47],"this":[48],"paper,":[49],"we":[50,74,89,98,128],"propose":[51],"asymmetric":[53,91],"dual-stream":[54,92],"networks":[55],"(MoADNet)":[56],"real-time":[58],"lightweight":[60,125],"SOD.":[62],"First,":[63],"inspired":[64],"the":[66,120,124,140,144,152,182],"intrinsic":[67],"discrepancy":[68],"between":[69],"modalities,":[73],"observe":[75],"that":[76],"maps":[78],"can":[79],"be":[80],"represented":[81],"fewer":[83],"channels":[84],"than":[85],"images.":[87],"Thus,":[88],"design":[90],"encoders":[93],"based":[94],"MobileNetV3.":[96],"Second,":[97],"develop":[99],"an":[100,113,130],"inverted":[101,114],"bottleneck":[102,115],"cross-modality":[103],"fusion":[104],"(IBCMF)":[105],"module":[106,136],"to":[107,117,137,158],"fuse":[108],"multimodality":[109],"features,":[110],"which":[111],"adopts":[112],"structure":[116],"compensate":[118],"loss":[122],"in":[123,151],"backbones.":[126],"Third,":[127],"present":[129],"adaptive":[131],"atrous":[132],"spatial":[133],"pyramid":[134],"(A2SP)":[135],"speed":[138],"up":[139],"inference,":[141],"maintaining":[143],"appropriately":[147],"selecting":[148],"multiscale":[149],"features":[150],"decoder.":[153],"Extensive":[154],"experiments":[155],"are":[156],"conducted":[157],"compare":[159],"our":[160],"method":[161,184],"with":[162],"15":[163],"state-of-the-art":[164],"approaches.":[165],"Our":[166],"MoADNet":[167,194],"obtains":[168],"competitive":[169],"results":[170],"five":[172],"benchmark":[173],"datasets":[174],"under":[175],"four":[176],"evaluation":[177],"metrics.":[178],"For":[179],"efficiency":[180],"analysis,":[181],"significantly":[185],"outperforms":[186],"other":[187],"baselines":[188],"a":[190,206,216],"large":[191],"margin.":[192],"The":[193],"only":[195],"contains":[196],"5.03":[197],"M":[198],"parameters":[199],"runs":[201],"80":[202],"FPS":[203],"when":[204],"testing":[205],"<inline-formula":[207],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[208],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">":[209],"<tex-math":[210],"notation=\"LaTeX\">$256\\times":[211],"256$":[212],"</tex-math></inline-formula>":[213],"image":[214],"single":[217],"NVIDIA":[218],"2080Ti":[219],"GPU.":[220]},"counts_by_year":[{"year":2026,"cited_by_count":24},{"year":2025,"cited_by_count":55},{"year":2024,"cited_by_count":32},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":3}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
