{"id":"https://openalex.org/W4200318028","doi":"https://doi.org/10.1109/tip.2021.3137660","title":"Adaptive Selection of Reference Frames for Video Object Segmentation","display_name":"Adaptive Selection of Reference Frames for Video Object Segmentation","publication_year":2021,"publication_date":"2021-12-29","ids":{"openalex":"https://openalex.org/W4200318028","doi":"https://doi.org/10.1109/tip.2021.3137660","pmid":"https://pubmed.ncbi.nlm.nih.gov/34965210"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2021.3137660","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2021.3137660","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5101804825","display_name":"Lingyi Hong","orcid":"https://orcid.org/0000-0002-2749-5133"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lingyi Hong","raw_affiliation_strings":["Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100441587","display_name":"Wei Zhang","orcid":"https://orcid.org/0000-0002-2358-8543"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Zhang","raw_affiliation_strings":["Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100780219","display_name":"Liangyu Chen","orcid":"https://orcid.org/0000-0003-0394-6208"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liangyu Chen","raw_affiliation_strings":["Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100669255","display_name":"Wenqiang Zhang","orcid":"https://orcid.org/0000-0002-3339-8751"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenqiang Zhang","raw_affiliation_strings":["Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100728564","display_name":"Jianping Fan","orcid":"https://orcid.org/0000-0002-4923-0910"},"institutions":[{"id":"https://openalex.org/I4210156165","display_name":"Lenovo (China)","ror":"https://ror.org/04srd9d93","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210156165"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianping Fan","raw_affiliation_strings":["AI Laboratory, Lenovo Research, Beijing, China"],"affiliations":[{"raw_affiliation_string":"AI Laboratory, Lenovo Research, Beijing, China","institution_ids":["https://openalex.org/I4210156165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101804825"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":2.2096,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.89929739,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"31","issue":null,"first_page":"1057","last_page":"1071"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7951507568359375},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7792373895645142},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7630928158760071},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.652283787727356},{"id":"https://openalex.org/keywords/reference-frame","display_name":"Reference frame","score":0.6506079435348511},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5779762268066406},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5705621838569641},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5536335110664368},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.48479360342025757},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.46220776438713074},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4150465726852417},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4100642204284668},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4027721881866455}],"concepts":[{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7951507568359375},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7792373895645142},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7630928158760071},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.652283787727356},{"id":"https://openalex.org/C172849965","wikidata":"https://www.wikidata.org/wiki/Q3148875","display_name":"Reference frame","level":3,"score":0.6506079435348511},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5779762268066406},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5705621838569641},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5536335110664368},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.48479360342025757},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.46220776438713074},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4150465726852417},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4100642204284668},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4027721881866455},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"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:10.1109/tip.2021.3137660","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2021.3137660","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:34965210","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34965210","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3628250287","display_name":null,"funder_award_id":"205111031020","funder_id":"https://openalex.org/F4320336652","funder_display_name":"Science and Technology Innovation Plan Of Shanghai Science and Technology Commission"},{"id":"https://openalex.org/G7105890812","display_name":null,"funder_award_id":"2020AAA0108301","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G7143284523","display_name":null,"funder_award_id":"62072112","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"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null},{"id":"https://openalex.org/F4320336652","display_name":"Science and Technology Innovation Plan Of Shanghai Science and Technology Commission","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W764651262","https://openalex.org/W1903029394","https://openalex.org/W1923697677","https://openalex.org/W1977028487","https://openalex.org/W2067107771","https://openalex.org/W2108598243","https://openalex.org/W2110158442","https://openalex.org/W2115606256","https://openalex.org/W2125215748","https://openalex.org/W2150355110","https://openalex.org/W2194775991","https://openalex.org/W2299400169","https://openalex.org/W2412782625","https://openalex.org/W2460260369","https://openalex.org/W2470139095","https://openalex.org/W2561860086","https://openalex.org/W2564998703","https://openalex.org/W2565639579","https://openalex.org/W2630837129","https://openalex.org/W2787091153","https://openalex.org/W2799157347","https://openalex.org/W2799239273","https://openalex.org/W2889658408","https://openalex.org/W2890447039","https://openalex.org/W2916743882","https://openalex.org/W2916797271","https://openalex.org/W2921536280","https://openalex.org/W2952122856","https://openalex.org/W2963253279","https://openalex.org/W2963503215","https://openalex.org/W2963548592","https://openalex.org/W2963732700","https://openalex.org/W2963983744","https://openalex.org/W2964157492","https://openalex.org/W2964218467","https://openalex.org/W2964309882","https://openalex.org/W2967622921","https://openalex.org/W2990205821","https://openalex.org/W3034798428","https://openalex.org/W3094664776","https://openalex.org/W3102457447","https://openalex.org/W3105065006","https://openalex.org/W3106773277","https://openalex.org/W3108819577","https://openalex.org/W3110030584","https://openalex.org/W3160550216","https://openalex.org/W4241071816","https://openalex.org/W4288374355","https://openalex.org/W6640295612","https://openalex.org/W6682082992","https://openalex.org/W6739696289","https://openalex.org/W6743811873","https://openalex.org/W6754033419","https://openalex.org/W6759534164","https://openalex.org/W6761623811","https://openalex.org/W6784713722"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W4246352526","https://openalex.org/W4390721878","https://openalex.org/W2965594636","https://openalex.org/W2810129309"],"abstract_inverted_index":{"Video":[0],"object":[1,122,167,237],"segmentation":[2],"is":[3,82,98,139,178,201],"a":[4,79,173,224],"challenging":[5,217],"task":[6],"in":[7,22,54,84,156,168],"computer":[8],"vision":[9],"because":[10],"the":[11,20,23,39,43,47,51,55,60,64,89,102,116,120,125,157,164,169,191,198,216,229],"appearances":[12],"of":[13,38,119,127,148,193,231],"target":[14,121,166],"objects":[15,147,155],"might":[16],"change":[17],"drastically":[18],"along":[19],"time":[21],"video.":[24,56],"To":[25,75],"solve":[26],"this":[27,77,85],"problem,":[28],"space-time":[29],"memory":[30,65],"(STM)":[31],"networks":[32],"are":[33],"exploited":[34],"to":[35,87,100,141,186,206,235],"make":[36,68],"use":[37],"information":[40,61,118],"from":[41,62,160,190],"all":[42,63],"intermediate":[44],"frames":[45,66,91,104],"between":[46],"first":[48],"frame":[49,53,159],"and":[50,108,123,131,137,151,223],"current":[52,158],"However,":[57],"fully":[58],"using":[59,181],"may":[67],"STM":[69],"not":[70],"practical":[71],"for":[72,146],"long":[73],"videos.":[74],"overcome":[76,124],"issue,":[78],"novel":[80,174],"method":[81],"developed":[83],"paper":[86],"select":[88],"reference":[90,103,170],"adaptively.":[92],"First,":[93],"an":[94,182],"adaptive":[95],"selection":[96],"criterion":[97],"introduced":[99],"choose":[101],"with":[105,163],"similar":[106,154],"appearance":[107,128],"precise":[109],"mask":[110],"estimation,":[111],"which":[112],"can":[113],"efficiently":[114],"capture":[115],"rich":[117],"challenges":[126],"changes,":[129],"occlusion,":[130],"model":[132],"drift.":[133],"Secondly,":[134],"bi-matching":[135],"(bi-scale":[136],"bi-direction)":[138],"conducted":[140],"obtain":[142,187],"more":[143],"robust":[144],"correlations":[145],"various":[149],"scales":[150],"prevents":[152],"multiple":[153],"being":[161],"mismatched":[162],"same":[165],"frame.":[171],"Thirdly,":[172],"edge":[175,183,194,199],"refinement":[176],"technique":[177],"designed":[179],"by":[180,211],"detection":[184],"network":[185],"smooth":[188,208],"edges":[189,209],"outputs":[192],"confidence":[195,200],"maps,":[196],"where":[197],"quantized":[202],"into":[203],"ten":[204],"sub-intervals":[205],"generate":[207],"step":[210],"step.":[212],"Experimental":[213],"results":[214],"on":[215],"benchmark":[218],"datasets":[219],"DAVIS-2016,":[220],"DAVIS-2017,":[221],"YouTube-VOS,":[222],"Long-Video":[225],"dataset":[226],"have":[227],"demonstrated":[228],"effectiveness":[230],"our":[232],"proposed":[233],"approach":[234],"video":[236],"segmentation.":[238]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
