{"id":"https://openalex.org/W4304092040","doi":"https://doi.org/10.1145/3503161.3547930","title":"Synthetic Data Supervised Salient Object Detection","display_name":"Synthetic Data Supervised Salient Object Detection","publication_year":2022,"publication_date":"2022-10-10","ids":{"openalex":"https://openalex.org/W4304092040","doi":"https://doi.org/10.1145/3503161.3547930"},"language":"en","primary_location":{"id":"doi:10.1145/3503161.3547930","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3547930","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2210.13835","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100773428","display_name":"Zhenyu Wu","orcid":"https://orcid.org/0000-0002-7990-500X"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhenyu Wu","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100403187","display_name":"Lin Wang","orcid":"https://orcid.org/0000-0002-6781-3335"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Wang","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100392006","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0002-5477-1017"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["Harbin Institute of Technology, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Shenzhen, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100615067","display_name":"Tengfei Shi","orcid":"https://orcid.org/0000-0002-4260-3905"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tengfei Shi","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030724247","display_name":"Chenglizhao Chen","orcid":"https://orcid.org/0000-0001-9982-5667"},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenglizhao Chen","raw_affiliation_strings":["China University of Petroleum (East China), Qingdao, China"],"affiliations":[{"raw_affiliation_string":"China University of Petroleum (East China), Qingdao, China","institution_ids":["https://openalex.org/I4210162190"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035153076","display_name":"Aimin Hao","orcid":"https://orcid.org/0000-0002-5774-6706"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aimin Hao","raw_affiliation_strings":["Beihang University & Peng Cheng Laboratory, Beijing & Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Beihang University & Peng Cheng Laboratory, Beijing & Shenzhen, China","institution_ids":["https://openalex.org/I4210136793","https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100386630","display_name":"Shuo Li","orcid":"https://orcid.org/0000-0002-5184-3230"},"institutions":[{"id":"https://openalex.org/I125749732","display_name":"Western University","ror":"https://ror.org/02grkyz14","country_code":"CA","type":"education","lineage":["https://openalex.org/I125749732"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Shuo Li","raw_affiliation_strings":["Western University, London, Canada"],"affiliations":[{"raw_affiliation_string":"Western University, London, Canada","institution_ids":["https://openalex.org/I125749732"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5100773428"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":1.4922,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.88256581,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5557","last_page":"5565"},"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":0.9828000068664551,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.963100016117096,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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":0.7311283349990845},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.6271989345550537},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.595908522605896},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5270490646362305},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5067515969276428},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43452996015548706},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.41182610392570496},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3213828504085541}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7311283349990845},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.6271989345550537},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.595908522605896},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5270490646362305},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5067515969276428},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43452996015548706},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41182610392570496},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3213828504085541}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3503161.3547930","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3547930","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Multimedia","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2210.13835","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2210.13835","pdf_url":"https://arxiv.org/pdf/2210.13835","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2210.13835","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2210.13835","pdf_url":"https://arxiv.org/pdf/2210.13835","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.7099999785423279,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1834627138","https://openalex.org/W1942214758","https://openalex.org/W1994922096","https://openalex.org/W2002781701","https://openalex.org/W2039313011","https://openalex.org/W2060095179","https://openalex.org/W2086791339","https://openalex.org/W2487365028","https://openalex.org/W2740667773","https://openalex.org/W2797761794","https://openalex.org/W2798791651","https://openalex.org/W2798825526","https://openalex.org/W2963112696","https://openalex.org/W2963136160","https://openalex.org/W2963693541","https://openalex.org/W2982083293","https://openalex.org/W2998449272","https://openalex.org/W3034239110","https://openalex.org/W3034431451","https://openalex.org/W3034453930","https://openalex.org/W3035422681","https://openalex.org/W3108318504","https://openalex.org/W3117869289","https://openalex.org/W3136838953","https://openalex.org/W3171087525","https://openalex.org/W3173143063","https://openalex.org/W3176009039","https://openalex.org/W3176679855","https://openalex.org/W3177087374","https://openalex.org/W3177221875","https://openalex.org/W3177399056","https://openalex.org/W3187090695","https://openalex.org/W3191739452","https://openalex.org/W3204376324","https://openalex.org/W3204446219","https://openalex.org/W3207493400","https://openalex.org/W4214506192","https://openalex.org/W4214680709","https://openalex.org/W4214687246","https://openalex.org/W4214762187"],"related_works":["https://openalex.org/W2329500892","https://openalex.org/W28991112","https://openalex.org/W2370726991","https://openalex.org/W2369710579","https://openalex.org/W4327728159","https://openalex.org/W4394266730","https://openalex.org/W1990856605","https://openalex.org/W2053783616","https://openalex.org/W4292830139","https://openalex.org/W4319309705"],"abstract_inverted_index":{"Although":[0],"deep":[1,10],"salient":[2],"object":[3],"detection":[4],"(SOD)":[5],"has":[6],"achieved":[7],"remarkable":[8],"progress,":[9],"SOD":[11,65,150],"models":[12],"are":[13],"extremely":[14],"data-hungry,":[15],"requiring":[16,46],"large-scale":[17],"pixel-wise":[18],"annotations":[19],"to":[20,61],"deliver":[21],"such":[22],"promising":[23],"results.":[24],"In":[25],"this":[26],"paper,":[27],"we":[28],"propose":[29],"a":[30,48,117,163,197],"novel":[31],"yet":[32],"effective":[33],"method":[34],"for":[35,85,167],"SOD,":[36],"coined":[37],"SODGAN,":[38],"which":[39,160],"can":[40,56,77,108,126,181],"generate":[41],"infinite":[42,110],"high-quality":[43],"image-mask":[44,130],"pairs":[45,55,131],"only":[47],"few":[49,118],"labeled":[50,119],"data,":[51],"and":[52,82,204],"these":[53],"synthesized":[54,129],"replace":[57],"the":[58,79,86,93,99,138,144,157,174,186,191],"human-labeled":[59],"DUTS-TR":[60],"train":[62],"any":[63],"off-the-shelf":[64],"model.":[66],"Its":[67],"contribution":[68],"is":[69,83,212],"three-fold.":[70],"1)":[71],"Our":[72,122],"proposed":[73,103,123],"diffusion":[74],"embedding":[75],"network":[76],"address":[78],"manifold":[80],"mismatch":[81],"tractable":[84],"latent":[87,95],"code":[88],"generation,":[89],"better":[90],"matching":[91],"with":[92,116,151],"ImageNet":[94],"space.":[96],"2)":[97],"For":[98,143],"first":[100,145],"time,":[101,146],"our":[102,147,194],"few-shot":[104],"saliency":[105,114,175,187],"mask":[106],"generator":[107],"synthesize":[109],"accurate":[111],"image":[112],"synchronized":[113],"masks":[115],"data.":[120,142],"3)":[121],"quality-aware":[124],"discriminator":[125],"select":[127],"highquality":[128],"from":[132,156],"noisy":[133],"synthetic":[134,141,152,179],"data":[135,153,180],"pool,":[136],"improving":[137],"quality":[139],"of":[140,185],"SODGAN":[148],"tackles":[149],"directly":[154],"generated":[155],"generative":[158],"model,":[159],"opens":[161],"up":[162],"new":[164,198],"research":[165],"paradigm":[166],"SOD.":[168],"Extensive":[169],"experimental":[170],"results":[171],"show":[172],"that":[173],"model":[176,188],"trained":[177,189],"on":[178,190],"achieve":[182],"$98.4%$":[183],"F-measure":[184],"DUTS-TR.":[192],"Moreover,":[193],"approach":[195],"achieves":[196],"SOTA":[199,209],"performance":[200],"in":[201],"semi/weakly-supervised":[202],"methods,":[203],"even":[205],"outperforms":[206],"several":[207],"fully-supervised":[208],"methods.":[210],"Code":[211],"available":[213],"at":[214],"https://github.com/wuzhenyubuaa/SODGAN":[215]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-13T07:58:08.660418","created_date":"2025-10-10T00:00:00"}