{"id":"https://openalex.org/W4391104468","doi":"https://doi.org/10.1145/3640816","title":"Semantic-Consistency-guided Learning on Deep Features for Unsupervised Salient Object Detection","display_name":"Semantic-Consistency-guided Learning on Deep Features for Unsupervised Salient Object Detection","publication_year":2024,"publication_date":"2024-01-22","ids":{"openalex":"https://openalex.org/W4391104468","doi":"https://doi.org/10.1145/3640816"},"language":"en","primary_location":{"id":"doi:10.1145/3640816","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3640816","pdf_url":null,"source":{"id":"https://openalex.org/S19610489","display_name":"ACM Transactions on Multimedia Computing Communications and Applications","issn_l":"1551-6857","issn":["1551-6857","1551-6865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Multimedia Computing, Communications, and Applications","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/A5101803988","display_name":"Ying Ying Zhang","orcid":"https://orcid.org/0000-0002-2704-7314"},"institutions":[{"id":"https://openalex.org/I4210110718","display_name":"Nanyang Normal University","ror":"https://ror.org/01f7yer47","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210110718"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ying Ying Zhang","raw_affiliation_strings":["School of Physics and Electronic Engineering, Nanyang Normal University, Henan Engineering Research Center for Radio Frequency Front End and Antenna of Millimeter Wave Wireless Communication System, Nan Yang, China"],"raw_orcid":"https://orcid.org/0000-0002-2704-7314","affiliations":[{"raw_affiliation_string":"School of Physics and Electronic Engineering, Nanyang Normal University, Henan Engineering Research Center for Radio Frequency Front End and Antenna of Millimeter Wave Wireless Communication System, Nan Yang, China","institution_ids":["https://openalex.org/I4210110718"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100379616","display_name":"Shuo Zhang","orcid":"https://orcid.org/0000-0003-4622-0669"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuo Zhang","raw_affiliation_strings":["School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4622-0669","affiliations":[{"raw_affiliation_string":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025273110","display_name":"Ming Hui","orcid":"https://orcid.org/0000-0003-2915-8656"},"institutions":[{"id":"https://openalex.org/I4210110718","display_name":"Nanyang Normal University","ror":"https://ror.org/01f7yer47","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210110718"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Hui","raw_affiliation_strings":["School of Physics and Electronic Engineering, Nanyang Normal University, Henan Engineering Research Center for Radio Frequency Front End and Antenna of Millimeter Wave Wireless Communication System, Nan Yang, China"],"raw_orcid":"https://orcid.org/0000-0003-2915-8656","affiliations":[{"raw_affiliation_string":"School of Physics and Electronic Engineering, Nanyang Normal University, Henan Engineering Research Center for Radio Frequency Front End and Antenna of Millimeter Wave Wireless Communication System, Nan Yang, China","institution_ids":["https://openalex.org/I4210110718"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101803988"],"corresponding_institution_ids":["https://openalex.org/I4210110718"],"apc_list":null,"apc_paid":null,"fwci":0.2244,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.43544316,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"20","issue":"6","first_page":"1","last_page":"23"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9822999835014343,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9678000211715698,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/computer-science","display_name":"Computer science","score":0.8986611366271973},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.6367067694664001},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6136026382446289},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5648834109306335},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.48301395773887634},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4828806221485138},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.45535990595817566},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.44452545046806335},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4182875156402588},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.36906832456588745},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3363994061946869},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2914637625217438}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8986611366271973},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.6367067694664001},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6136026382446289},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5648834109306335},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.48301395773887634},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4828806221485138},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.45535990595817566},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.44452545046806335},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4182875156402588},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.36906832456588745},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3363994061946869},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2914637625217438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3640816","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3640816","pdf_url":null,"source":{"id":"https://openalex.org/S19610489","display_name":"ACM Transactions on Multimedia Computing Communications and Applications","issn_l":"1551-6857","issn":["1551-6857","1551-6865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Multimedia Computing, Communications, and Applications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8056419873","display_name":null,"funder_award_id":"61702289","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W1588168368","https://openalex.org/W1745334888","https://openalex.org/W1772076007","https://openalex.org/W1903001680","https://openalex.org/W1903029394","https://openalex.org/W2047670868","https://openalex.org/W2055180303","https://openalex.org/W2128272608","https://openalex.org/W2153110463","https://openalex.org/W2203271703","https://openalex.org/W2282640221","https://openalex.org/W2412094331","https://openalex.org/W2560092514","https://openalex.org/W2569272946","https://openalex.org/W2585592883","https://openalex.org/W2617634260","https://openalex.org/W2735033384","https://openalex.org/W2756626360","https://openalex.org/W2758611985","https://openalex.org/W2766439555","https://openalex.org/W2780708736","https://openalex.org/W2798791651","https://openalex.org/W2804743778","https://openalex.org/W2805199679","https://openalex.org/W2891071241","https://openalex.org/W2896169497","https://openalex.org/W2917790938","https://openalex.org/W2938260698","https://openalex.org/W2963032190","https://openalex.org/W2963781443","https://openalex.org/W2963868681","https://openalex.org/W2963906836","https://openalex.org/W2976065006","https://openalex.org/W2991044292","https://openalex.org/W3003590623","https://openalex.org/W3034185160","https://openalex.org/W3083948783","https://openalex.org/W3108318504","https://openalex.org/W3125520697","https://openalex.org/W3132018008","https://openalex.org/W3136838953","https://openalex.org/W3156231362","https://openalex.org/W3158200550","https://openalex.org/W4205688290","https://openalex.org/W4226080771","https://openalex.org/W4226216113","https://openalex.org/W4226354398","https://openalex.org/W4239147634","https://openalex.org/W4256361765"],"related_works":["https://openalex.org/W2329500892","https://openalex.org/W28991112","https://openalex.org/W2370726991","https://openalex.org/W2949096641","https://openalex.org/W2970686063","https://openalex.org/W4320729701","https://openalex.org/W4220926404","https://openalex.org/W3123344745","https://openalex.org/W3034745255","https://openalex.org/W4254103348"],"abstract_inverted_index":{"Unsupervised":[0],"salient":[1,35],"object":[2,36],"detection":[3,37],"is":[4,16,134,256],"an":[5],"important":[6],"task":[7],"in":[8,202],"many":[9],"real-world":[10],"scenarios":[11],"where":[12,95],"pixel-wise":[13],"label":[14],"information":[15],"of":[17,48,60,76,120,141,213],"scarce":[18],"availability.":[19],"Despite":[20],"its":[21],"significance,":[22],"this":[23,81],"problem":[24],"remains":[25],"rarely":[26],"explored,":[27],"with":[28,258],"a":[29,85,128,203],"few":[30],"works":[31],"that":[32,244],"consider":[33],"unsupervised":[34,92,247],"methods":[38,56],"based":[39],"on":[40,191],"the":[41,45,58,61,67,74,77,96,107,116,121,125,147,162,167,174,179,185,192,196,209,214,220,230,245,252],"fused":[42,78,122],"graph":[43],"from":[44],"sum":[46],"fusion":[47,88,131],"multiple":[49,101,142,180],"deep":[50,144,182,188,261],"feature":[51,63,103],"similarity":[52,64,70,104,110,153,169,194],"matrices.":[53],"However,":[54],"these":[55],"ignore":[57],"interrelation":[59],"low-level":[62,102,143,181],"matrices":[65,105,154,170],"and":[66,98,106,118,139,146,184,224,255],"high-level":[68,108,148],"semantic":[69,109,149,187],"matrice,":[71],"which":[72],"degrades":[73],"quality":[75,119],"graph.":[79,123],"In":[80,124,161],"article,":[82],"we":[83],"propose":[84],"semantic-consistency-guided":[86,129,152],"multi-graph":[87,130],"learning":[89,132],"algorithm":[90],"for":[91,157,234],"saliency":[93,159,164,198,235],"detection,":[94],"consistency":[97,138],"inconsistency":[99,140],"between":[100],"matrice":[111],"are":[112,155,171,200],"explored":[113],"to":[114,136,177],"promote":[115],"robustness":[117],"first":[126],"stage,":[127,166],"method":[133,248],"proposed":[135,246],"exploit":[137],"features":[145,183],"feature.":[150,189],"The":[151],"computed":[156],"preliminary":[158,221],"ranking.":[160],"following":[163],"refinement":[165,197,225],"semantic-enhanced":[168,193,204],"built":[172],"by":[173],"cross":[175],"diffusion":[176],"fuse":[178],"high":[186],"Based":[190],"matrices,":[195],"maps":[199],"calculated":[201],"cellular":[205],"automata":[206],"manner.":[207],"Furthermore,":[208],"final":[210],"ensemble":[211],"stage":[212],"large":[215,231],"margin":[216,232],"semi-supervised":[217],"classification":[218],"views":[219],"ranking":[222],"results":[223,226],"as":[227],"features,":[228],"adopts":[229],"graphs":[233],"ensemble.":[236],"Extensive":[237],"evaluations":[238],"over":[239],"four":[240],"benchmark":[241],"datasets":[242],"show":[243],"performs":[249],"favorably":[250],"against":[251],"state-of-the-art":[253],"approaches":[254],"competitive":[257],"some":[259],"supervised":[260],"learning-based":[262],"methods.":[263]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
