{"id":"https://openalex.org/W1961270558","doi":"https://doi.org/10.1109/cvpr.2015.7299057","title":"Multiclass semantic video segmentation with object-level active inference","display_name":"Multiclass semantic video segmentation with object-level active inference","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W1961270558","doi":"https://doi.org/10.1109/cvpr.2015.7299057","mag":"1961270558"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2015.7299057","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7299057","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5089927825","display_name":"Buyu Liu","orcid":"https://orcid.org/0009-0004-5534-7463"},"institutions":[{"id":"https://openalex.org/I42894916","display_name":"Data61","ror":"https://ror.org/03q397159","country_code":"AU","type":"other","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I42894916","https://openalex.org/I4387156119"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Buyu Liu","raw_affiliation_strings":["ANU/NICTA","ANU/NICTA, Canberra ACT 0200, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ANU/NICTA","institution_ids":["https://openalex.org/I42894916"]},{"raw_affiliation_string":"ANU/NICTA, Canberra ACT 0200, Australia","institution_ids":["https://openalex.org/I42894916"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015970030","display_name":"Xuming He","orcid":"https://orcid.org/0000-0003-2150-1237"},"institutions":[{"id":"https://openalex.org/I42894916","display_name":"Data61","ror":"https://ror.org/03q397159","country_code":"AU","type":"other","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I42894916","https://openalex.org/I4387156119"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xuming He","raw_affiliation_strings":["ANU/NICTA","NICTA/ANU, Canberra ACT 0200, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ANU/NICTA","institution_ids":["https://openalex.org/I42894916"]},{"raw_affiliation_string":"NICTA/ANU, Canberra ACT 0200, Australia","institution_ids":["https://openalex.org/I42894916"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I42894916"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":69,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4286","last_page":"4294"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11714","display_name":"Multimodal Machine Learning Applications","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/T10036","display_name":"Advanced Neural Network Applications","score":0.9987000226974487,"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":0.7703293561935425},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6621710062026978},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6616551876068115},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6466498374938965},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5641739964485168},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5200788378715515},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.49528685212135315},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.42162856459617615},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34275394678115845}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7703293561935425},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6621710062026978},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6616551876068115},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6466498374938965},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5641739964485168},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5200788378715515},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.49528685212135315},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.42162856459617615},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34275394678115845}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr.2015.7299057","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7299057","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.6700000166893005,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320315885","display_name":"Australian Government","ror":"https://ror.org/0314h5y94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W61823270","https://openalex.org/W191249702","https://openalex.org/W250737475","https://openalex.org/W801273237","https://openalex.org/W1479789543","https://openalex.org/W1551429860","https://openalex.org/W1578197944","https://openalex.org/W1610707153","https://openalex.org/W1691567762","https://openalex.org/W1849585967","https://openalex.org/W1857926807","https://openalex.org/W1913356549","https://openalex.org/W1973071925","https://openalex.org/W1986834574","https://openalex.org/W1989684337","https://openalex.org/W1998793490","https://openalex.org/W1999874108","https://openalex.org/W2068994826","https://openalex.org/W2076874408","https://openalex.org/W2083597815","https://openalex.org/W2096979710","https://openalex.org/W2104266970","https://openalex.org/W2108599331","https://openalex.org/W2129085190","https://openalex.org/W2137881638","https://openalex.org/W2140239908","https://openalex.org/W2157431481","https://openalex.org/W2161236525","https://openalex.org/W2171815231","https://openalex.org/W2534859116","https://openalex.org/W2536208356","https://openalex.org/W2586680856","https://openalex.org/W2952793010","https://openalex.org/W4285719527","https://openalex.org/W4293390546","https://openalex.org/W6622862201","https://openalex.org/W6628729794","https://openalex.org/W6632783177","https://openalex.org/W6637566013","https://openalex.org/W6639073361","https://openalex.org/W6639824712","https://openalex.org/W6675892410","https://openalex.org/W6680357304","https://openalex.org/W6683607639","https://openalex.org/W6685043703","https://openalex.org/W6728583406"],"related_works":["https://openalex.org/W2019566805","https://openalex.org/W2383464976","https://openalex.org/W1669643531","https://openalex.org/W2101128524","https://openalex.org/W2122581818","https://openalex.org/W2004370856","https://openalex.org/W2026019026","https://openalex.org/W1631910785","https://openalex.org/W1967061043","https://openalex.org/W1963494852"],"abstract_inverted_index":{"We":[0,43,95,126],"address":[1],"the":[2,54,83,91,97],"problem":[3,98],"of":[4,67,114,120],"integrating":[5],"object":[6,39,57,68,88],"reasoning":[7],"with":[8],"supervoxel":[9,41,55],"labeling":[10],"in":[11,26,90],"multiclass":[12,134],"semantic":[13,136],"video":[14,135],"segmentation.":[15],"To":[16,70],"this":[17],"end,":[18],"we":[19,75],"first":[20],"propose":[21],"an":[22,45,77,106],"object-augmented":[23,92],"dense":[24,93],"CRF":[25],"spatio-temporal":[27],"domain,":[28],"which":[29,85,104],"captures":[30],"long-range":[31],"dependency":[32],"between":[33,38],"supervoxels,":[34],"and":[35,40,59,117,123,139,143],"imposes":[36],"consistency":[37],"labels.":[42],"develop":[44],"efficient":[46],"mean":[47],"field":[48],"inference":[49,79],"algorithm":[50],"to":[51,81],"jointly":[52],"infer":[53],"labels,":[56],"activations":[58],"their":[60],"occlusion":[61],"relations":[62],"for":[63],"a":[64,100,112,118],"moderate":[65],"number":[66],"hypotheses.":[69],"scale":[71],"up":[72],"our":[73,128],"method,":[74],"adopt":[76],"active":[78],"strategy":[80],"improve":[82],"efficiency,":[84],"adaptively":[86],"selects":[87],"subgraphs":[89],"CRF.":[94],"formulate":[96],"as":[99],"Markov":[101],"Decision":[102],"Process,":[103],"learns":[105],"approximate":[107],"optimal":[108],"policy":[109],"based":[110],"on":[111,130],"reward":[113],"accuracy":[115],"improvement":[116],"set":[119],"well-designed":[121],"model":[122],"input":[124],"features.":[125],"evaluate":[127],"method":[129],"three":[131],"publicly":[132],"available":[133],"segmentation":[137],"datasets":[138],"demonstrate":[140],"superior":[141],"efficiency":[142],"accuracy.":[144]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":9},{"year":2017,"cited_by_count":18},{"year":2016,"cited_by_count":16}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
