{"id":"https://openalex.org/W3004686108","doi":"https://doi.org/10.1109/access.2020.2971964","title":"Video Object Segmentation by Latent Outcome Regression","display_name":"Video Object Segmentation by Latent Outcome Regression","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3004686108","doi":"https://doi.org/10.1109/access.2020.2971964","mag":"3004686108"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.2971964","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2971964","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08985334.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08985334.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100351772","display_name":"Lin Zhang","orcid":"https://orcid.org/0000-0001-5151-9273"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Zhang","raw_affiliation_strings":["Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5151-9273","affiliations":[{"raw_affiliation_string":"Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101882875","display_name":"Yao Lu","orcid":"https://orcid.org/0000-0003-3760-1638"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Lu","raw_affiliation_strings":["Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.3833,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.59353298,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":96},"biblio":{"volume":"8","issue":null,"first_page":"30355","last_page":"30367"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9998999834060669,"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":0.9998999834060669,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9991999864578247,"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":0.9980000257492065,"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":0.7870638370513916},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7094647884368896},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6659606695175171},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4958730638027191},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4929537773132324},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48004066944122314},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.46620261669158936},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.45563286542892456},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4514178931713104},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44229230284690857},{"id":"https://openalex.org/keywords/market-segmentation","display_name":"Market segmentation","score":0.4227762222290039},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4157186448574066},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.07629939913749695}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7870638370513916},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7094647884368896},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6659606695175171},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4958730638027191},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4929537773132324},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48004066944122314},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.46620261669158936},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.45563286542892456},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4514178931713104},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44229230284690857},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.4227762222290039},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4157186448574066},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.07629939913749695},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.2971964","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2971964","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08985334.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:51e3127408dc466d9ee1a7fd9128881e","is_oa":true,"landing_page_url":"https://doaj.org/article/51e3127408dc466d9ee1a7fd9128881e","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 30355-30367 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.2971964","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2971964","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08985334.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4373497954","display_name":null,"funder_award_id":"61273273","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":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3004686108.pdf","grobid_xml":"https://content.openalex.org/works/W3004686108.grobid-xml"},"referenced_works_count":77,"referenced_works":["https://openalex.org/W7746136","https://openalex.org/W170496991","https://openalex.org/W589665618","https://openalex.org/W625543958","https://openalex.org/W1496571393","https://openalex.org/W1904248166","https://openalex.org/W1920142129","https://openalex.org/W1941318923","https://openalex.org/W1943880421","https://openalex.org/W1954128991","https://openalex.org/W1975862670","https://openalex.org/W1982075130","https://openalex.org/W1989348325","https://openalex.org/W1991329653","https://openalex.org/W1991367009","https://openalex.org/W2009874829","https://openalex.org/W2016075127","https://openalex.org/W2016163842","https://openalex.org/W2030346542","https://openalex.org/W2061035601","https://openalex.org/W2062563118","https://openalex.org/W2081239020","https://openalex.org/W2088780408","https://openalex.org/W2089961441","https://openalex.org/W2096979710","https://openalex.org/W2102304169","https://openalex.org/W2102840968","https://openalex.org/W2106229755","https://openalex.org/W2110019070","https://openalex.org/W2113708607","https://openalex.org/W2118490033","https://openalex.org/W2123330025","https://openalex.org/W2127707449","https://openalex.org/W2129305389","https://openalex.org/W2129587342","https://openalex.org/W2131747574","https://openalex.org/W2138682569","https://openalex.org/W2155598147","https://openalex.org/W2156252543","https://openalex.org/W2167331599","https://openalex.org/W2187051054","https://openalex.org/W2210596465","https://openalex.org/W2212077366","https://openalex.org/W2283283146","https://openalex.org/W2293605988","https://openalex.org/W2322739735","https://openalex.org/W2402395722","https://openalex.org/W2460260369","https://openalex.org/W2462481369","https://openalex.org/W2463175074","https://openalex.org/W2470139095","https://openalex.org/W2510637518","https://openalex.org/W2524130909","https://openalex.org/W2562457735","https://openalex.org/W2564998703","https://openalex.org/W2588240745","https://openalex.org/W2610147486","https://openalex.org/W2737008123","https://openalex.org/W2750515003","https://openalex.org/W2763794872","https://openalex.org/W2799157347","https://openalex.org/W2895293811","https://openalex.org/W2895340898","https://openalex.org/W2911981226","https://openalex.org/W2963253279","https://openalex.org/W2963548592","https://openalex.org/W2963551464","https://openalex.org/W2963917006","https://openalex.org/W2963983744","https://openalex.org/W2964157492","https://openalex.org/W2964218467","https://openalex.org/W2997487053","https://openalex.org/W6606968713","https://openalex.org/W6683555033","https://openalex.org/W6697018430","https://openalex.org/W6753214761","https://openalex.org/W6772357780"],"related_works":["https://openalex.org/W4386190339","https://openalex.org/W2968424575","https://openalex.org/W3142333283","https://openalex.org/W2580650124","https://openalex.org/W3122088529","https://openalex.org/W3041320102","https://openalex.org/W2111669074","https://openalex.org/W2085259108","https://openalex.org/W3123087812","https://openalex.org/W2071416974"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,16,65,85,91,103,161],"novel":[4],"algorithm":[5,94,173,207,235,255],"for":[6,95,110,125,144,185],"unsupervised":[7],"video":[8],"object":[9],"segmentation":[10,97,214],"(UVOS)":[11],"in":[12,24,39,50,64,84,102,114,147,240],"unconstrained":[13],"scenarios.":[14],"Although":[15],"large":[17],"variety":[18],"of":[19,62,80,132,164,171,181,192,196],"methods":[20,35,83,184,198,215,268],"have":[21],"been":[22],"proposed":[23],"the":[25,48,60,78,121,130,136,157,165,179,194,204,249,264],"literature,":[26],"segmenting":[27],"generic":[28],"objects":[29],"is":[30,154,174,190,208],"still":[31],"challenging":[32],"because":[33],"different":[34,40,182],"often":[36],"perform":[37],"well":[38],"situations,":[41],"and":[42,105,127,222,260],"no":[43,224],"single":[44,112,183,267],"method":[45,113],"can":[46,72,256],"outperform":[47],"others":[49],"all":[51],"cases.":[52],"To":[53],"address":[54],"this,":[55],"we":[56,89],"propose":[57,90],"to":[58,159],"solve":[59],"problem":[61],"UVOS":[63],"crowd-sourcing":[66],"setting.":[67],"We":[68],"claim":[69],"that":[70,175,233],"one":[71],"achieve":[73,257],"superior":[74],"results":[75,228],"by":[76,98,262],"aggregating":[77],"predictions":[79],"multiple":[81],"imperfect":[82],"reasonable":[86],"way.":[87],"Specifically,":[88],"latent":[92],"regression":[93,126,158],"ensemble-based":[96],"jointly":[99],"labelling":[100],"pixels":[101],"sequence":[104],"learning":[106,163,213,252],"an":[107,115],"adaptive":[108,250],"weight":[109,133,251],"each":[111,186],"ensemble.":[116],"The":[117,167],"pixel":[118],"labellings":[119],"offer":[120],"outcome":[122],"(pseudo":[123],"groundtruth)":[124],"thus":[128,189],"promote":[129],"procedure":[131],"learning,":[134],"while":[135,199],"learnt":[137],"weights":[138],"could":[139],"provide":[140],"better":[141],"shape":[142],"priors":[143],"labelling,":[145],"resulting":[146],"more":[148],"accurate":[149],"segmentation.":[150],"Besides,":[151],"Laplacian":[152],"regularization":[153],"introduced":[155],"into":[156],"facilitate":[160],"stable":[162],"weights.":[166],"most":[168,265],"distinct":[169],"feature":[170],"our":[172,206,234,254],"it":[176],"adaptively":[177],"learns":[178],"contributions":[180],"test":[187],"sequence,":[188],"capable":[191],"capturing":[193],"advantages":[195],"those":[197],"avoiding":[200],"their":[201],"weaknesses.":[202],"In":[203],"experiments,":[205],"built":[209],"on":[210,219,229],"14":[211],"non-deep":[212],"which":[216],"are":[217],"based":[218],"handcrafted":[220],"features":[221],"require":[223],"training":[225],"data.":[226],"Experimental":[227],"popular":[230],"benchmarks":[231],"show":[232],"achieves":[236],"compelling":[237],"performance,":[238],"even":[239],"comparison":[241],"with":[242],"deep":[243],"learning-based":[244],"methods.":[245],"Furthermore,":[246],"benefiting":[247],"from":[248],"mechanism,":[253],"good":[258],"flexibility":[259],"usability":[261],"choosing":[263],"complementary":[266],"without":[269],"losing":[270],"too":[271],"much":[272],"performance.":[273]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}