{"id":"https://openalex.org/W2914466670","doi":"https://doi.org/10.1109/tcsvt.2019.2894363","title":"Objects Discovery Based on Co-Occurrence Word Model With Anchor-Box Polishing","display_name":"Objects Discovery Based on Co-Occurrence Word Model With Anchor-Box Polishing","publication_year":2019,"publication_date":"2019-01-24","ids":{"openalex":"https://openalex.org/W2914466670","doi":"https://doi.org/10.1109/tcsvt.2019.2894363","mag":"2914466670"},"language":"en","primary_location":{"id":"doi:10.1109/tcsvt.2019.2894363","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2019.2894363","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"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 Circuits and Systems for Video Technology","raw_type":"journal-article"},"type":"article","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/A5101857588","display_name":"Zhewei Zhang","orcid":"https://orcid.org/0000-0003-0117-6668"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhewei Zhang","raw_affiliation_strings":["Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0117-6668","affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080987681","display_name":"Tao Jing","orcid":"https://orcid.org/0000-0001-5277-1729"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Jing","raw_affiliation_strings":["Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113965474","display_name":"Chunhua Tian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chunhua Tian","raw_affiliation_strings":["K2data Ltd., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"K2data Ltd., Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053338656","display_name":"Peng\u2010Fei Cui","orcid":"https://orcid.org/0000-0001-7925-0202"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pengfei Cui","raw_affiliation_strings":["K2data Ltd., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"K2data Ltd., Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100628867","display_name":"Xuejing Li","orcid":"https://orcid.org/0000-0003-0254-141X"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuejing Li","raw_affiliation_strings":["Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033468930","display_name":"Meilin Gao","orcid":"https://orcid.org/0000-0001-6474-8518"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meilin Gao","raw_affiliation_strings":["Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2034,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.51558096,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"30","issue":"3","first_page":"632","last_page":"645"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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":0.9994000196456909,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.8577760457992554},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6196144819259644},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5454627871513367},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5437865853309631},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5124707818031311},{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.5118852853775024},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.5066645741462708},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4245333671569824},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41860431432724},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.41550254821777344},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35648804903030396},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.30484646558761597},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.19565078616142273}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8577760457992554},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6196144819259644},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5454627871513367},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5437865853309631},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5124707818031311},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.5118852853775024},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.5066645741462708},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4245333671569824},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41860431432724},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.41550254821777344},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35648804903030396},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.30484646558761597},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.19565078616142273},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcsvt.2019.2894363","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2019.2894363","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"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 Circuits and Systems for Video Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W809122546","https://openalex.org/W1536680647","https://openalex.org/W1594098193","https://openalex.org/W1742311587","https://openalex.org/W1924999439","https://openalex.org/W1936290543","https://openalex.org/W1966229885","https://openalex.org/W1974042113","https://openalex.org/W1975001816","https://openalex.org/W1978276805","https://openalex.org/W1995168330","https://openalex.org/W1996517960","https://openalex.org/W1999035929","https://openalex.org/W2006410278","https://openalex.org/W2035866593","https://openalex.org/W2038500908","https://openalex.org/W2045377163","https://openalex.org/W2062805169","https://openalex.org/W2068730032","https://openalex.org/W2071486728","https://openalex.org/W2081332440","https://openalex.org/W2095014960","https://openalex.org/W2098894541","https://openalex.org/W2101309634","https://openalex.org/W2102605133","https://openalex.org/W2108451171","https://openalex.org/W2109255472","https://openalex.org/W2112301665","https://openalex.org/W2113137767","https://openalex.org/W2125027820","https://openalex.org/W2126391637","https://openalex.org/W2130306094","https://openalex.org/W2134731454","https://openalex.org/W2141834896","https://openalex.org/W2142077116","https://openalex.org/W2143516773","https://openalex.org/W2149035855","https://openalex.org/W2155979701","https://openalex.org/W2163665255","https://openalex.org/W2170146448","https://openalex.org/W2179352600","https://openalex.org/W2592418885","https://openalex.org/W2593841437","https://openalex.org/W2742980673","https://openalex.org/W2755486969","https://openalex.org/W2962834831","https://openalex.org/W2963446712","https://openalex.org/W2963762195","https://openalex.org/W3098722327","https://openalex.org/W3106250896","https://openalex.org/W4231510805","https://openalex.org/W4248936881","https://openalex.org/W4293584584","https://openalex.org/W4297775537","https://openalex.org/W6622964371","https://openalex.org/W6639619044","https://openalex.org/W6676066654","https://openalex.org/W6679349572","https://openalex.org/W6681016373","https://openalex.org/W6681769576","https://openalex.org/W6737664043","https://openalex.org/W7062670285"],"related_works":["https://openalex.org/W2769501189","https://openalex.org/W4315588616","https://openalex.org/W4312773271","https://openalex.org/W2888805565","https://openalex.org/W2962686197","https://openalex.org/W3005513013","https://openalex.org/W2207653751","https://openalex.org/W3159709618","https://openalex.org/W2611137333","https://openalex.org/W4389543811"],"abstract_inverted_index":{"The":[0,37,161],"state-of-the-art":[1],"objects":[2,42,88,188],"discovery":[3,43,89],"approaches":[4],"can":[5,57,171,201],"be":[6,58,202],"categorized":[7],"into":[8],"two:":[9],"deep":[10,38],"learning":[11,25,39],"methods":[12,26,40,66],"based":[13,27,45],"on":[14,28,46,112,163],"a":[15,86,93,103],"convolutional":[16],"neural":[17],"network":[18],"with":[19,106,157,204],"region":[20],"proposals,":[21],"and":[22,53,80,96,180,194],"conventional":[23,65,155],"machine":[24],"topic":[29,104,151],"models,":[30],"low-rank":[31],"matrix":[32],"factorization,":[33],"or":[34],"image":[35],"processing.":[36],"for":[41],"are":[44],"sacrificing":[47],"computational":[48],"complexity":[49,79],"to":[50,148,187],"achieve":[51],"precision,":[52],"the":[54,64,74,77,81,100,113,123,127,133,144,149,168,173,198,215],"training":[55,78,101],"time":[56],"long":[59],"without":[60],"GPU":[61],"platforms,":[62],"whereas":[63],"usually":[67],"lack":[68],"detection":[69,82,145,159,174,209],"accuracy.":[70],"To":[71],"effectively":[72],"address":[73],"problems":[75],"of":[76,115,178],"speed,":[83],"we":[84,136],"present":[85],"new":[87],"approach":[90,170],"by":[91],"proposing":[92],"two-stage":[94],"(training":[95],"verification)":[97],"method.":[98],"In":[99,132],"stage,":[102,135],"model":[105,152],"words":[107],"co-occurrence":[108,124],"prior":[109],"is":[110,129,184],"proposed":[111,169,199],"basis":[114],"Latent":[116],"Dirichlet":[117],"Allocation":[118],"(LDA)":[119],"model,":[120],"in":[121,176,190,211],"which":[122,212],"information":[125],"among":[126],"features":[128],"sufficiently":[130],"utilized.":[131],"verification":[134],"propose":[137],"an":[138],"anchor-box":[139],"polishing":[140],"algorithm":[141],"that":[142,167],"fine-tunes":[143],"results":[146],"corresponding":[147],"pre-trained":[150],"from":[153],"some":[154],"algorithms":[156],"fast":[158,206],"time.":[160],"experiments":[162],"various":[164],"datasets":[165],"demonstrate":[166],"improve":[172],"performance":[175],"terms":[177],"efficiency":[179],"computing":[181],"costs.":[182],"It":[183],"also":[185],"robust":[186],"different":[189],"colors,":[191],"lightings,":[192],"scales,":[193],"so":[195],"on.":[196],"Interestingly,":[197],"method":[200],"combined":[203],"many":[205],"but":[207],"inaccurate":[208],"algorithms,":[210],"it":[213],"enhances":[214],"model's":[216],"flexibility.":[217]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}