{"id":"https://openalex.org/W2905437926","doi":"https://doi.org/10.1109/lsp.2018.2886470","title":"Multi-Scale Guided Mask Refinement for Coarse-to-Fine RGB-D Perception","display_name":"Multi-Scale Guided Mask Refinement for Coarse-to-Fine RGB-D Perception","publication_year":2018,"publication_date":"2018-12-12","ids":{"openalex":"https://openalex.org/W2905437926","doi":"https://doi.org/10.1109/lsp.2018.2886470","mag":"2905437926"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2018.2886470","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2018.2886470","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","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/A5008393475","display_name":"Chongyu Chen","orcid":"https://orcid.org/0000-0002-7080-9783"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chongyu Chen","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-7080-9783","affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018922655","display_name":"Haoguang Huang","orcid":"https://orcid.org/0000-0002-1729-918X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoguang Huang","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-1729-918X","affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069427000","display_name":"Chuangrong Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuangrong Chen","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081720431","display_name":"Zhuoqi Zheng","orcid":"https://orcid.org/0000-0002-6061-7112"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuoqi Zheng","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-6061-7112","affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101409148","display_name":"Hui Cheng","orcid":"https://orcid.org/0000-0003-2579-7004"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Cheng","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-2579-7004","affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":{"value":2645,"currency":"USD","value_usd":2645},"apc_paid":null,"fwci":0.3683,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.64217776,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"26","issue":"2","first_page":"217","last_page":"221"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9995999932289124,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9995999932289124,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9995999932289124,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9990000128746033,"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":0.8086830973625183},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7783079147338867},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6706387996673584},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.630282998085022},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6159660220146179},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.45299550890922546},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.423159122467041},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3570469617843628}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8086830973625183},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7783079147338867},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6706387996673584},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.630282998085022},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6159660220146179},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.45299550890922546},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.423159122467041},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3570469617843628}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2018.2886470","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2018.2886470","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G492365670","display_name":null,"funder_award_id":"61602533","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6098546482","display_name":null,"funder_award_id":"1614050001452","funder_id":"https://openalex.org/F4320321921","funder_display_name":"Natural Science Foundation of Guangdong Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321921","display_name":"Natural Science Foundation of Guangdong Province","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":34,"referenced_works":["https://openalex.org/W338829346","https://openalex.org/W1565402342","https://openalex.org/W1861837994","https://openalex.org/W1969366022","https://openalex.org/W1973326669","https://openalex.org/W1985912834","https://openalex.org/W1986342589","https://openalex.org/W2028282671","https://openalex.org/W2076756823","https://openalex.org/W2079920317","https://openalex.org/W2097007083","https://openalex.org/W2104208370","https://openalex.org/W2110500134","https://openalex.org/W2123432765","https://openalex.org/W2124592697","https://openalex.org/W2131720600","https://openalex.org/W2137574036","https://openalex.org/W2141572457","https://openalex.org/W2146055337","https://openalex.org/W2322480645","https://openalex.org/W2412782625","https://openalex.org/W2473131906","https://openalex.org/W2521178580","https://openalex.org/W2568271872","https://openalex.org/W2605929543","https://openalex.org/W2702174010","https://openalex.org/W2950186728","https://openalex.org/W2950981687","https://openalex.org/W3099037876","https://openalex.org/W4299566741","https://openalex.org/W6633727509","https://openalex.org/W6639271887","https://openalex.org/W6679724742","https://openalex.org/W6700594562"],"related_works":["https://openalex.org/W4285411112","https://openalex.org/W2085033728","https://openalex.org/W2171299904","https://openalex.org/W4390494008","https://openalex.org/W2922442631","https://openalex.org/W2053596378","https://openalex.org/W2168523118","https://openalex.org/W2073639911","https://openalex.org/W2106540031","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Pixel-level":[0],"object":[1,185],"segmentation":[2,11,88,111,121],"is":[3,29,60,170],"highly":[4],"desired":[5],"in":[6,22,55],"many":[7],"vision":[8],"applications.":[9],"Although":[10],"methods":[12,51,65],"purely":[13],"based":[14,64],"on":[15,140,172,219],"visual":[16,56],"input":[17],"have":[18],"achieved":[19],"great":[20],"success":[21],"the":[23,33,40,110,141,156,178,188,220],"past":[24],"decade,":[25],"their":[26],"further":[27],"improvement":[28],"still":[30],"hindered":[31],"by":[32,126,138],"intrinsic":[34],"drawback":[35],"of":[36,46,143,159,222],"color":[37,73,83],"camouflage.":[38],"With":[39],"rapid":[41],"development":[42],"and":[43,74,84,93,115,163,184],"wide":[44],"deployment":[45],"depth":[47,49,75,85],"sensors,":[48],"assisted":[50],"are":[52,76],"increasingly":[53],"popular":[54],"perception":[57,232],"systems.":[58,233],"It":[59],"expected":[61],"that":[62,153,193],"RGB-D":[63,215],"can":[66,122,134,196],"lead":[67],"to":[68,81,100,108,202],"significant":[69,198],"performance":[70],"improvements":[71,200],"because":[72,118],"naturally":[77],"complementary.":[78],"However,":[79],"how":[80],"merge":[82],"modalities":[86],"for":[87,213,229],"with":[89],"both":[90,160],"high":[91,94],"efficiency":[92],"accuracy":[95,199],"remains":[96],"an":[97,210],"open":[98],"problem":[99,137],"be":[101,123],"addressed.":[102],"In":[103,130,146],"this":[104,131,136],"letter,":[105],"we":[106,133,148],"propose":[107,149],"divide":[109],"process":[112],"into":[113],"\u201ccoarse\u201d":[114],"\u201crefining\u201d":[116],"stages":[117],"a":[119,150],"coarse":[120,144,179],"easily":[124],"obtained":[125],"various":[127],"light-weight":[128,231],"methods.":[129],"way,":[132],"tackle":[135],"focusing":[139],"refinement":[142,224],"segmentations.":[145],"particular,":[147],"multi-scale":[151],"approach":[152,169,195],"selectively":[154],"inherits":[155],"effective":[157,211],"features":[158],"edge-preserving":[161,207],"filtering":[162],"deep":[164],"neural":[165],"networks.":[166],"The":[167],"proposed":[168],"evaluated":[171],"several":[173],"benchmark":[174],"datasets,":[175],"respectively,":[176],"using":[177],"segmentations":[180],"from":[181],"background":[182],"subtraction":[183],"detection":[186],"as":[187],"input.":[189],"Numerous":[190],"results":[191],"indicate":[192],"our":[194,217],"achieve":[197],"compared":[201],"other":[203],"alternatives,":[204],"demonstrating":[205],"superior":[206],"capability.":[208],"Besides":[209],"method":[212],"merging":[214],"information,":[216],"study":[218],"capability":[221],"coarse-to-fine":[223],"also":[225],"brings":[226],"new":[227],"inspirations":[228],"designing":[230]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}
