{"id":"https://openalex.org/W2902156419","doi":"https://doi.org/10.1109/tcyb.2018.2879859","title":"Embedding Attention and Residual Network for Accurate Salient Object Detection","display_name":"Embedding Attention and Residual Network for Accurate Salient Object Detection","publication_year":2018,"publication_date":"2018-11-27","ids":{"openalex":"https://openalex.org/W2902156419","doi":"https://doi.org/10.1109/tcyb.2018.2879859","mag":"2902156419","pmid":"https://pubmed.ncbi.nlm.nih.gov/30507520"},"language":"en","primary_location":{"id":"doi:10.1109/tcyb.2018.2879859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcyb.2018.2879859","pdf_url":null,"source":{"id":"https://openalex.org/S4210191041","display_name":"IEEE Transactions on Cybernetics","issn_l":"2168-2267","issn":["2168-2267","2168-2275"],"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 Cybernetics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5030758562","display_name":"Shuhan Chen","orcid":"https://orcid.org/0000-0002-0094-5157"},"institutions":[{"id":"https://openalex.org/I78978612","display_name":"Yangzhou University","ror":"https://ror.org/03tqb8s11","country_code":"CN","type":"education","lineage":["https://openalex.org/I78978612"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuhan Chen","raw_affiliation_strings":["School of Information Engineering, Yangzhou University, Yangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-0094-5157","affiliations":[{"raw_affiliation_string":"School of Information Engineering, Yangzhou University, Yangzhou, China","institution_ids":["https://openalex.org/I78978612"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101639270","display_name":"Ben Wang","orcid":"https://orcid.org/0000-0002-1354-0634"},"institutions":[{"id":"https://openalex.org/I78978612","display_name":"Yangzhou University","ror":"https://ror.org/03tqb8s11","country_code":"CN","type":"education","lineage":["https://openalex.org/I78978612"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ben Wang","raw_affiliation_strings":["School of Information Engineering, Yangzhou University, Yangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-1354-0634","affiliations":[{"raw_affiliation_string":"School of Information Engineering, Yangzhou University, Yangzhou, China","institution_ids":["https://openalex.org/I78978612"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037494981","display_name":"Xiuli Tan","orcid":null},"institutions":[{"id":"https://openalex.org/I78978612","display_name":"Yangzhou University","ror":"https://ror.org/03tqb8s11","country_code":"CN","type":"education","lineage":["https://openalex.org/I78978612"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiuli Tan","raw_affiliation_strings":["School of Information Engineering, Yangzhou University, Yangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Yangzhou University, Yangzhou, China","institution_ids":["https://openalex.org/I78978612"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057230452","display_name":"Xuelong Hu","orcid":"https://orcid.org/0000-0002-5556-2612"},"institutions":[{"id":"https://openalex.org/I78978612","display_name":"Yangzhou University","ror":"https://ror.org/03tqb8s11","country_code":"CN","type":"education","lineage":["https://openalex.org/I78978612"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuelong Hu","raw_affiliation_strings":["School of Information Engineering, Yangzhou University, Yangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Yangzhou University, Yangzhou, China","institution_ids":["https://openalex.org/I78978612"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I78978612"],"apc_list":null,"apc_paid":null,"fwci":3.6415,"has_fulltext":false,"cited_by_count":70,"citation_normalized_percentile":{"value":0.95078221,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"50","issue":"5","first_page":"2050","last_page":"2062"},"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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.9753000140190125,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T11094","display_name":"Face Recognition and Perception","score":0.9690999984741211,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7825709581375122},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6557148098945618},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6514211297035217},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.6461593508720398},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5370904803276062},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5223520994186401},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5209885239601135},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4835759997367859},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4585554003715515},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4482285976409912},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.43406862020492554},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.43247532844543457},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36076152324676514},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3333476781845093},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.21548694372177124}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7825709581375122},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6557148098945618},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6514211297035217},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.6461593508720398},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5370904803276062},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5223520994186401},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5209885239601135},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4835759997367859},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4585554003715515},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4482285976409912},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.43406862020492554},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.43247532844543457},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36076152324676514},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3333476781845093},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.21548694372177124},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tcyb.2018.2879859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcyb.2018.2879859","pdf_url":null,"source":{"id":"https://openalex.org/S4210191041","display_name":"IEEE Transactions on Cybernetics","issn_l":"2168-2267","issn":["2168-2267","2168-2275"],"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 Cybernetics","raw_type":"journal-article"},{"id":"pmid:30507520","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/30507520","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 cybernetics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.44999998807907104,"id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G3841401138","display_name":null,"funder_award_id":"61802336","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6063833822","display_name":null,"funder_award_id":"2017CXJ026","funder_id":"https://openalex.org/F4320324130","funder_display_name":"Yangzhou University"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320324130","display_name":"Yangzhou University","ror":"https://ror.org/03tqb8s11"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":80,"referenced_works":["https://openalex.org/W845365781","https://openalex.org/W937276653","https://openalex.org/W1473786217","https://openalex.org/W1514535095","https://openalex.org/W1686810756","https://openalex.org/W1772076007","https://openalex.org/W1903029394","https://openalex.org/W1942214758","https://openalex.org/W1947031653","https://openalex.org/W1948751323","https://openalex.org/W1996326832","https://openalex.org/W2037954058","https://openalex.org/W2039313011","https://openalex.org/W2047670868","https://openalex.org/W2072761058","https://openalex.org/W2086791339","https://openalex.org/W2121927366","https://openalex.org/W2133515615","https://openalex.org/W2147347517","https://openalex.org/W2155893237","https://openalex.org/W2157381954","https://openalex.org/W2161185676","https://openalex.org/W2161236525","https://openalex.org/W2194775991","https://openalex.org/W2270657321","https://openalex.org/W2293332611","https://openalex.org/W2294182682","https://openalex.org/W2302086703","https://openalex.org/W2307770531","https://openalex.org/W2322480645","https://openalex.org/W2338972621","https://openalex.org/W2461475918","https://openalex.org/W2508741746","https://openalex.org/W2511589228","https://openalex.org/W2517325737","https://openalex.org/W2518599539","https://openalex.org/W2519528544","https://openalex.org/W2520188835","https://openalex.org/W2565639579","https://openalex.org/W2591692311","https://openalex.org/W2604414494","https://openalex.org/W2605929543","https://openalex.org/W2613299509","https://openalex.org/W2740652190","https://openalex.org/W2740667773","https://openalex.org/W2742556866","https://openalex.org/W2744613561","https://openalex.org/W2748624060","https://openalex.org/W2752782242","https://openalex.org/W2766439555","https://openalex.org/W2780708736","https://openalex.org/W2799907879","https://openalex.org/W2884412194","https://openalex.org/W2952793010","https://openalex.org/W2962835968","https://openalex.org/W2962891704","https://openalex.org/W2962917547","https://openalex.org/W2962992847","https://openalex.org/W2963032190","https://openalex.org/W2963299740","https://openalex.org/W2963420686","https://openalex.org/W2963446712","https://openalex.org/W2963495494","https://openalex.org/W2963635628","https://openalex.org/W2963868681","https://openalex.org/W2963906836","https://openalex.org/W2963954913","https://openalex.org/W2964105113","https://openalex.org/W3098389804","https://openalex.org/W3103757879","https://openalex.org/W3103912303","https://openalex.org/W3104979525","https://openalex.org/W4212906384","https://openalex.org/W4239147634","https://openalex.org/W6630875275","https://openalex.org/W6637373629","https://openalex.org/W6725549056","https://openalex.org/W6738381068","https://openalex.org/W6743130548","https://openalex.org/W6784647857"],"related_works":["https://openalex.org/W2329500892","https://openalex.org/W28991112","https://openalex.org/W2370726991","https://openalex.org/W2081900870","https://openalex.org/W2369710579","https://openalex.org/W2560215812","https://openalex.org/W4327728159","https://openalex.org/W4394266730","https://openalex.org/W1990856605","https://openalex.org/W2053783616"],"abstract_inverted_index":{"Salient":[0],"object":[1,73,111,243],"detection":[2,60,112,244],"is":[3,96,134],"usually":[4,91],"used":[5],"as":[6,65,189],"a":[7,12,38,118,137,168,194,208],"preprocessing":[8],"step":[9,186,188],"to":[10,36,58,122,147,172,180,200],"facilitate":[11],"variety":[13],"of":[14,27,46,256],"subsequent":[15],"applications":[16],"which":[17,95,140],"should":[18],"take":[19],"little":[20],"time":[21,97],"cost.":[22],"With":[23],"the":[24,43,47,162,174,230,248,254],"quick":[25],"development":[26],"deep":[28,49],"learning":[29,125],"recently,":[30],"profound":[31],"progresses":[32],"have":[33],"been":[34],"made":[35],"achieve":[37],"new":[39],"state-of-the-art":[40],"performance.":[41],"However,":[42],"learned":[44,175],"features":[45,177,204],"existing":[48],"learning-based":[50],"methods":[51],"are":[52,90],"not":[53,211],"accurate":[54],"enough":[55],"thus":[56],"leading":[57],"unsatisfactory":[59],"in":[61,126,136,161,245,253],"complex":[62],"scenes,":[63],"such":[64],"low":[66],"contrast":[67],"or":[68,183],"very":[69,249],"similar":[70],"between":[71],"salient":[72,80,110,153,242],"and":[74,77,155,237],"background":[75,163],"region":[76],"multiple":[78],"(small)":[79],"objects":[81,154],"with":[82,247],"diverse":[83],"characteristics.":[84],"In":[85,130],"addition,":[86],"some":[87],"post-processing":[88],"techniques":[89],"needed":[92],"for":[93,205],"refinement,":[94],"consuming.":[98],"To":[99],"address":[100],"these":[101],"issues,":[102],"this":[103],"paper":[104],"presents":[105],"an":[106],"efficient":[107,214],"fully":[108],"convolutional":[109],"network.":[113],"Specifically,":[114],"we":[115,166,192],"first":[116],"introduce":[117,193],"visual":[119],"attention":[120,132],"mechanism":[121],"guide":[123],"feature":[124],"side":[127],"output":[128],"layers.":[129],"detail,":[131],"weight":[133],"employed":[135],"top-down":[138],"manner":[139],"can":[141],"bridge":[142],"high":[143],"level":[144],"semantic":[145],"information":[146],"help":[148],"shallow":[149],"layers":[150],"better":[151],"locate":[152],"also":[156,218],"filter":[157],"out":[158],"noisy":[159],"response":[160],"region.":[164],"Second,":[165],"propose":[167],"residual":[169,203],"refinement":[170],"network":[171,220],"fuse":[173],"multilevel":[176],"gradually.":[178],"Not":[179],"simply":[181],"add":[182],"concatenate":[184],"them":[185],"by":[187],"previous":[190],"works,":[191],"second-order":[195,209],"term":[196,210],"into":[197],"element-wise":[198],"addition":[199],"learn":[201],"stage-wise":[202],"refinement.":[206],"Such":[207],"only":[212],"benefits":[213],"gradient":[215],"propagation":[216],"but":[217],"increases":[219],"nonlinearity.":[221],"Extensive":[222],"experiments":[223],"on":[224,240],"seven":[225],"standard":[226],"benchmarks":[227],"demonstrate":[228],"that":[229],"proposed":[231],"approach":[232],"achieves":[233],"consistently":[234],"superior":[235],"performance":[236],"performs":[238],"well":[239],"small":[241],"comparison":[246],"recent":[250],"state-of-the-arts,":[251],"especially":[252],"metric":[255],"structure-measure.":[257]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":18},{"year":2019,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}