{"id":"https://openalex.org/W2884944276","doi":"https://doi.org/10.1109/tmm.2018.2859746","title":"Refinet: A Deep Segmentation Assisted Refinement Network for Salient Object Detection","display_name":"Refinet: A Deep Segmentation Assisted Refinement Network for Salient Object Detection","publication_year":2018,"publication_date":"2018-07-25","ids":{"openalex":"https://openalex.org/W2884944276","doi":"https://doi.org/10.1109/tmm.2018.2859746","mag":"2884944276"},"language":"en","primary_location":{"id":"doi:10.1109/tmm.2018.2859746","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2018.2859746","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","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/A5042631401","display_name":"Keren Fu","orcid":"https://orcid.org/0000-0002-3195-2077"},"institutions":[{"id":"https://openalex.org/I4210125143","display_name":"Chengdu University","ror":"https://ror.org/034z67559","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210125143"]},{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Keren Fu","raw_affiliation_strings":["College of Computer Science, Sichuan University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Sichuan University, Chengdu, China","institution_ids":["https://openalex.org/I4210125143","https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085914001","display_name":"Qijun Zhao","orcid":"https://orcid.org/0000-0003-4651-7163"},"institutions":[{"id":"https://openalex.org/I4210125143","display_name":"Chengdu University","ror":"https://ror.org/034z67559","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210125143"]},{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qijun Zhao","raw_affiliation_strings":["College of Computer Science, Sichuan University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Sichuan University, Chengdu, China","institution_ids":["https://openalex.org/I4210125143","https://openalex.org/I24185976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044267089","display_name":"Irene Yu\u2010Hua Gu","orcid":"https://orcid.org/0000-0003-4759-7038"},"institutions":[{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Irene Yu-Hua Gu","raw_affiliation_strings":["Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden","institution_ids":["https://openalex.org/I66862912"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5042631401"],"corresponding_institution_ids":["https://openalex.org/I24185976","https://openalex.org/I4210125143"],"apc_list":null,"apc_paid":null,"fwci":4.3872,"has_fulltext":false,"cited_by_count":65,"citation_normalized_percentile":{"value":0.96086131,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"21","issue":"2","first_page":"457","last_page":"469"},"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":0.9934999942779541,"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":0.9789000153541565,"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/computer-science","display_name":"Computer science","score":0.8624027967453003},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.7841542959213257},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7695703506469727},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.7687016725540161},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6647898554801941},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6436055898666382},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.641656756401062},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6156214475631714},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5583857893943787},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5514041185379028},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5272323489189148},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5059873461723328},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.49282410740852356},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4543730914592743},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.43369781970977783},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4116542637348175},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3573125898838043},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2119603157043457}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8624027967453003},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.7841542959213257},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7695703506469727},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.7687016725540161},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6647898554801941},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6436055898666382},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.641656756401062},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6156214475631714},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5583857893943787},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5514041185379028},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5272323489189148},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5059873461723328},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.49282410740852356},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4543730914592743},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.43369781970977783},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4116542637348175},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3573125898838043},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2119603157043457},{"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},{"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/tmm.2018.2859746","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2018.2859746","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","raw_type":"journal-article"},{"id":"pmh:oai:research.chalmers.se:503926","is_oa":false,"landing_page_url":"https://research.chalmers.se/en/publication/503926","pdf_url":null,"source":{"id":"https://openalex.org/S4306402469","display_name":"Chalmers Research (Chalmers University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66862912","host_organization_name":"Chalmers University of Technology","host_organization_lineage":["https://openalex.org/I66862912"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""},{"id":"pmh:oai:research.chalmers.se:508634","is_oa":false,"landing_page_url":"http://doi.org/10.1109/TMM.2018.2859746","pdf_url":null,"source":{"id":"https://openalex.org/S4306402469","display_name":"Chalmers Research (Chalmers University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66862912","host_organization_name":"Chalmers University of Technology","host_organization_lineage":["https://openalex.org/I66862912"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""},{"id":"pmh:oai:research.chalmers.se:510119","is_oa":false,"landing_page_url":"https://research.chalmers.se/en/publication/fd5d5db8-ca31-4a19-a705-532a37fc9c85","pdf_url":null,"source":{"id":"https://openalex.org/S4306402469","display_name":"Chalmers Research (Chalmers University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66862912","host_organization_name":"Chalmers University of Technology","host_organization_lineage":["https://openalex.org/I66862912"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2092262562","display_name":null,"funder_award_id":"61773270","funder_id":"https://openalex.org/F4320335353","funder_display_name":"National Science Foundation of Sri Lanka"},{"id":"https://openalex.org/G4725216253","display_name":null,"funder_award_id":"61703077","funder_id":"https://openalex.org/F4320335353","funder_display_name":"National Science Foundation of Sri Lanka"},{"id":"https://openalex.org/G4742868982","display_name":null,"funder_award_id":"YJ201755","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G7051809588","display_name":null,"funder_award_id":"2016YFC0801100","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G8056387268","display_name":null,"funder_award_id":"2017YFB0802300","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G8411809184","display_name":null,"funder_award_id":"2013YQ49087904","funder_id":"https://openalex.org/F4320335765","funder_display_name":"National Key Scientific Instrument and Equipment Development Projects of China"}],"funders":[{"id":"https://openalex.org/F4320335353","display_name":"National Science Foundation of Sri Lanka","ror":"https://ror.org/010xaa060"},{"id":"https://openalex.org/F4320335765","display_name":"National Key Scientific Instrument and Equipment Development Projects of China","ror":null},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","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":91,"referenced_works":["https://openalex.org/W21025885","https://openalex.org/W892735016","https://openalex.org/W1665214252","https://openalex.org/W1686810756","https://openalex.org/W1772076007","https://openalex.org/W1894057436","https://openalex.org/W1932188298","https://openalex.org/W1934184906","https://openalex.org/W1942214758","https://openalex.org/W1947031653","https://openalex.org/W1963841606","https://openalex.org/W1965301399","https://openalex.org/W1974536722","https://openalex.org/W1978249862","https://openalex.org/W1981228217","https://openalex.org/W1982075130","https://openalex.org/W1996326832","https://openalex.org/W1999478155","https://openalex.org/W2002574940","https://openalex.org/W2026019603","https://openalex.org/W2037954058","https://openalex.org/W2039313011","https://openalex.org/W2058359403","https://openalex.org/W2065372998","https://openalex.org/W2067191022","https://openalex.org/W2086791339","https://openalex.org/W2098702446","https://openalex.org/W2100470808","https://openalex.org/W2104600947","https://openalex.org/W2107363596","https://openalex.org/W2110158442","https://openalex.org/W2112796928","https://openalex.org/W2117301471","https://openalex.org/W2117539524","https://openalex.org/W2118246710","https://openalex.org/W2121378474","https://openalex.org/W2125188192","https://openalex.org/W2125378844","https://openalex.org/W2125647562","https://openalex.org/W2126532873","https://openalex.org/W2128272608","https://openalex.org/W2129587342","https://openalex.org/W2131297486","https://openalex.org/W2147347517","https://openalex.org/W2155893237","https://openalex.org/W2156777442","https://openalex.org/W2161185676","https://openalex.org/W2161236525","https://openalex.org/W2163605009","https://openalex.org/W2164084182","https://openalex.org/W2194775991","https://openalex.org/W2293332611","https://openalex.org/W2294182682","https://openalex.org/W2338972621","https://openalex.org/W2395611524","https://openalex.org/W2415053570","https://openalex.org/W2437041077","https://openalex.org/W2461475918","https://openalex.org/W2486803733","https://openalex.org/W2519528544","https://openalex.org/W2519788000","https://openalex.org/W2592997798","https://openalex.org/W2606302360","https://openalex.org/W2618530766","https://openalex.org/W2740652190","https://openalex.org/W2740667773","https://openalex.org/W2744613561","https://openalex.org/W2754188632","https://openalex.org/W2765161239","https://openalex.org/W2772161954","https://openalex.org/W2777511827","https://openalex.org/W2779744667","https://openalex.org/W2780708736","https://openalex.org/W2952793010","https://openalex.org/W2962835968","https://openalex.org/W2963032190","https://openalex.org/W2963299740","https://openalex.org/W2996076706","https://openalex.org/W3103604207","https://openalex.org/W3103757879","https://openalex.org/W3104979525","https://openalex.org/W4239147634","https://openalex.org/W4241716071","https://openalex.org/W6600850345","https://openalex.org/W6637242042","https://openalex.org/W6637373629","https://openalex.org/W6639359414","https://openalex.org/W6651042577","https://openalex.org/W6726799623","https://openalex.org/W6726839451","https://openalex.org/W6772035322"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W147410782","https://openalex.org/W3022252430","https://openalex.org/W3103989898","https://openalex.org/W4287804464","https://openalex.org/W803346624","https://openalex.org/W3021077427"],"abstract_inverted_index":{"Compared":[0,141],"to":[1,18,31,47,98,110,131,142,152],"conventional":[2],"saliency":[3,19,44,85,139,155],"detection":[4,20],"by":[5,158,171],"handcrafted":[6],"features,":[7],"deep":[8,67],"convolutional":[9,78,102],"neural":[10],"networks":[11],"(CNNs)":[12],"recently":[13],"have":[14],"been":[15],"successfully":[16],"applied":[17],"field":[21],"with":[22,81,161],"superior":[23],"performance":[24],"on":[25,76,174],"locating":[26],"salient":[27],"objects.":[28,56],"However,":[29],"due":[30],"repeated":[32],"sub-sampling":[33],"operations":[34],"inside":[35],"CNNs":[36],"such":[37],"as":[38],"pooling":[39,94],"and":[40,52,95,107,115,166],"convolution,":[41],"many":[42],"CNN-based":[43],"models":[45,160],"fail":[46],"maintain":[48],"fine-grained":[49],"spatial":[50,164],"details":[51,114],"boundary":[53,134],"structures":[54],"of":[55,121,136],"To":[57],"remedy":[58],"this":[59,61],"issue,":[60],"paper":[62],"proposes":[63],"a":[64,99],"novel":[65],"end-to-end":[66],"learning-based":[68],"refinement":[69],"model":[70],"named":[71],"Refinet,":[72],"which":[73],"is":[74,128,150,169],"based":[75],"fully":[77,101],"network":[79,103],"augmented":[80],"segmentation":[82],"hypotheses.":[83],"Intermediate":[84],"maps":[86,123,156],"that":[87],"are":[88,90],"edge-aware":[89],"computed":[91],"from":[92],"segmentation-based":[93],"then":[96],"feed":[97],"two-tier":[100],"for":[104],"effective":[105],"fusion":[106],"refinement,":[108],"leading":[109],"more":[111,162],"precise":[112],"object":[113],"boundaries.":[116],"In":[117],"addition,":[118],"the":[119,125,137],"resolution":[120],"feature":[122],"in":[124],"proposed":[126],"Refinet":[127,149],"carefully":[129],"designed":[130],"guarantee":[132],"sufficient":[133],"clarity":[135],"refined":[138],"output.":[140],"widely":[143],"employed":[144],"dense":[145],"conditional":[146],"random":[147],"field,":[148],"able":[151],"enhance":[153],"coarse":[154],"generated":[157],"existing":[159],"accurate":[163],"details,":[165],"its":[167],"effectiveness":[168],"demonstrated":[170],"experimental":[172],"results":[173],"seven":[175],"benchmark":[176],"datasets.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":24},{"year":2019,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}