{"id":"https://openalex.org/W2904487290","doi":"https://doi.org/10.1609/aaai.v33i01.33018569","title":"SuperVAE: Superpixelwise Variational Autoencoder for Salient Object Detection","display_name":"SuperVAE: Superpixelwise Variational Autoencoder for Salient Object Detection","publication_year":2019,"publication_date":"2019-07-17","ids":{"openalex":"https://openalex.org/W2904487290","doi":"https://doi.org/10.1609/aaai.v33i01.33018569","mag":"2904487290"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v33i01.33018569","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33018569","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v33i01.33018569","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100758169","display_name":"Bo Li","orcid":"https://orcid.org/0000-0001-5980-4861"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Li","raw_affiliation_strings":["Nanjing University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073588743","display_name":"Zhengxing Sun","orcid":"https://orcid.org/0000-0001-7137-6169"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengxing Sun","raw_affiliation_strings":["Nanjing University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025924166","display_name":"Yuqi Guo","orcid":"https://orcid.org/0000-0002-7060-8404"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuqi Guo","raw_affiliation_strings":["Nanjing University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.549,"has_fulltext":true,"cited_by_count":46,"citation_normalized_percentile":{"value":0.93728223,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"33","issue":"01","first_page":"8569","last_page":"8576"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9519000053405762,"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/autoencoder","display_name":"Autoencoder","score":0.8722438216209412},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8049185276031494},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7953305840492249},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6878466606140137},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6689672470092773},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6559240221977234},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.644171953201294},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5997770428657532},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5786762833595276},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.5115540623664856},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4254278242588043},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3635517954826355},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3267122507095337},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06488776206970215}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8722438216209412},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8049185276031494},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7953305840492249},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6878466606140137},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6689672470092773},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6559240221977234},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.644171953201294},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5997770428657532},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5786762833595276},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.5115540623664856},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4254278242588043},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3635517954826355},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3267122507095337},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06488776206970215},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaai.v33i01.33018569","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33018569","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:ojs.aaai.org:article/4876","is_oa":true,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/4876","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/4876/4749","source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1609/aaai.v33i01.33018569","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33018569","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3405531881","display_name":null,"funder_award_id":"ZZKT2013A12","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6667581590","display_name":null,"funder_award_id":"61321491","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6732323027","display_name":null,"funder_award_id":"2007AA01Z334","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6859443155","display_name":"\u57fa\u4e8e\u51e0\u4f55\u7406\u89e3\u7684\u4ea4\u4e92\u5f0f\u5b9e\u4f8b\u5efa\u6a21\u6280\u672f\u7814\u7a76","funder_award_id":"61272219","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W21025885","https://openalex.org/W1686810756","https://openalex.org/W1854404533","https://openalex.org/W1903029394","https://openalex.org/W1942214758","https://openalex.org/W1959608418","https://openalex.org/W1996326832","https://openalex.org/W2047670868","https://openalex.org/W2055180303","https://openalex.org/W2065372998","https://openalex.org/W2080142539","https://openalex.org/W2100470808","https://openalex.org/W2109255472","https://openalex.org/W2117539524","https://openalex.org/W2133515615","https://openalex.org/W2179352600","https://openalex.org/W2282640221","https://openalex.org/W2293332611","https://openalex.org/W2294182682","https://openalex.org/W2338972621","https://openalex.org/W2472480899","https://openalex.org/W2475287302","https://openalex.org/W2518599539","https://openalex.org/W2552652233","https://openalex.org/W2588600710","https://openalex.org/W2742555060","https://openalex.org/W2754559068","https://openalex.org/W2789006531","https://openalex.org/W2808442315","https://openalex.org/W2963152757","https://openalex.org/W2963223306","https://openalex.org/W2963906836","https://openalex.org/W3104979525","https://openalex.org/W3125520697","https://openalex.org/W4308909683","https://openalex.org/W6600850345","https://openalex.org/W6640610931","https://openalex.org/W6640963894","https://openalex.org/W6649627412","https://openalex.org/W6674505857","https://openalex.org/W6688384872","https://openalex.org/W6697567838","https://openalex.org/W6730005914","https://openalex.org/W6731638882","https://openalex.org/W6742480443"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2953234277","https://openalex.org/W2734887215","https://openalex.org/W2803255133","https://openalex.org/W2810679507"],"abstract_inverted_index":{"Image":[0],"saliency":[1,136],"detection":[2,68],"has":[3],"recently":[4],"witnessed":[5],"rapid":[6],"progress":[7],"due":[8,37],"to":[9,38,81,112,119,167],"deep":[10,23,46,116],"neural":[11,29],"networks.":[12],"However,":[13],"there":[14],"still":[15],"exist":[16],"many":[17],"important":[18],"problems":[19],"in":[20],"the":[21,39,83,92,95,125,146,158,171],"existing":[22],"learning":[24,47],"based":[25],"methods.":[26,176],"Pixel-wise":[27],"convolutional":[28,40],"network":[30],"(CNN)":[31],"methods":[32,48],"suffer":[33],"from":[34,91,115],"blurry":[35],"boundaries":[36,143],"and":[41,86,102,144],"pooling":[42],"operations.":[43],"While":[44],"region-based":[45],"lack":[49],"spatial":[50,103,147],"consistency":[51],"since":[52],"they":[53],"deal":[54],"with":[55],"each":[56],"region":[57],"independently.":[58],"In":[59],"this":[60],"paper,":[61],"we":[62,106],"propose":[63,108],"a":[64,71,109],"novel":[65],"salient":[66,89],"object":[67,142],"framework":[69],"using":[70],"superpixelwise":[72],"variational":[73],"autoencoder":[74],"(SuperVAE)":[75],"network.":[76,123],"We":[77],"first":[78],"use":[79],"VAE":[80],"model":[82],"image":[84],"background":[85,93],"then":[87],"separate":[88],"objects":[90],"through":[94],"reconstruction":[96],"residuals.":[97],"To":[98],"better":[99,140],"capture":[100],"semantic":[101],"contexts":[104],"information,":[105],"also":[107],"perceptual":[110],"loss":[111],"take":[113],"advantage":[114],"pre-trained":[117],"CNNs":[118],"train":[120],"our":[121,131],"SuperVAE":[122],"Without":[124],"supervision":[126],"of":[127],"mask-level":[128],"annotated":[129],"data,":[130],"method":[132,160],"generates":[133],"high":[134],"quality":[135],"results":[137],"which":[138],"can":[139],"preserve":[141],"maintain":[145],"consistency.":[148],"Extensive":[149],"experiments":[150],"on":[151],"five":[152],"wildly-used":[153],"benchmark":[154],"datasets":[155],"show":[156],"that":[157],"proposed":[159],"achieves":[161],"superior":[162],"or":[163],"competitive":[164],"performance":[165],"compared":[166],"other":[168],"algorithms":[169],"including":[170],"very":[172],"recent":[173],"state-of-the-art":[174],"supervised":[175]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":13},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}