{"id":"https://openalex.org/W3001226180","doi":"https://doi.org/10.1109/vcip47243.2019.8965801","title":"Joint Learning of Dictionary and Convolutional Network for Pedestrian Attribute Recognition","display_name":"Joint Learning of Dictionary and Convolutional Network for Pedestrian Attribute Recognition","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3001226180","doi":"https://doi.org/10.1109/vcip47243.2019.8965801","mag":"3001226180"},"language":"en","primary_location":{"id":"doi:10.1109/vcip47243.2019.8965801","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip47243.2019.8965801","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Visual Communications and Image Processing (VCIP)","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":null,"display_name":"Yan Sha","orcid":null},"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":"Yan Sha","raw_affiliation_strings":["Beijing Jiaotong University,Beijing,China","Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023442981","display_name":"Congyan Lang","orcid":"https://orcid.org/0000-0001-6059-7943"},"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":"Congyan Lang","raw_affiliation_strings":["Beijing Jiaotong University,Beijing,China","Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070755370","display_name":"Peixi Peng","orcid":"https://orcid.org/0000-0002-7427-8764"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peixi Peng","raw_affiliation_strings":["Chinese Academy of Sciences,Institute of Automation,Beijing,China","Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Institute of Automation,Beijing,China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]},{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210094879"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076090670","display_name":"Junliang Xing","orcid":"https://orcid.org/0000-0001-6801-0510"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junliang Xing","raw_affiliation_strings":["Chinese Academy of Sciences,Institute of Automation,Beijing,China","Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Institute of Automation,Beijing,China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]},{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210094879"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088774924","display_name":"Danxia Li","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Danxia Li","raw_affiliation_strings":["Peking University,Beijing,China","Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University,Beijing,China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9969000220298767,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9962000250816345,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8513906598091125},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.6758410334587097},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6750129461288452},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6530998945236206},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.5909048318862915},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.5279456973075867},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5186620950698853},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.49982261657714844},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.48752495646476746},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4619297385215759},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.4412645697593689}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8513906598091125},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6758410334587097},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6750129461288452},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6530998945236206},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.5909048318862915},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.5279456973075867},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5186620950698853},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.49982261657714844},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.48752495646476746},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4619297385215759},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.4412645697593689},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vcip47243.2019.8965801","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip47243.2019.8965801","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.5199999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W262462045","https://openalex.org/W1887734902","https://openalex.org/W1967988963","https://openalex.org/W1999533590","https://openalex.org/W2058102599","https://openalex.org/W2085660690","https://openalex.org/W2098411764","https://openalex.org/W2111025459","https://openalex.org/W2117539524","https://openalex.org/W2154579312","https://openalex.org/W2194775991","https://openalex.org/W2286727787","https://openalex.org/W2308869522","https://openalex.org/W2410968923","https://openalex.org/W2519809734","https://openalex.org/W2549365021","https://openalex.org/W2549401308","https://openalex.org/W2558681897","https://openalex.org/W2732937366","https://openalex.org/W2739088263","https://openalex.org/W2867270703","https://openalex.org/W2889143500","https://openalex.org/W2896249043","https://openalex.org/W2963365374","https://openalex.org/W2963620099","https://openalex.org/W2963790258","https://openalex.org/W2964248351","https://openalex.org/W6609767918","https://openalex.org/W6682751323","https://openalex.org/W6714781226","https://openalex.org/W6741936186","https://openalex.org/W6752623288","https://openalex.org/W6754364688"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W2101960027","https://openalex.org/W1980381208","https://openalex.org/W2197846993","https://openalex.org/W49697837","https://openalex.org/W2586575957","https://openalex.org/W3122828758","https://openalex.org/W2972620127","https://openalex.org/W2981141433"],"abstract_inverted_index":{"Pedestrian":[0],"attribute":[1,83,126],"recognition":[2],"is":[3,66,87,94,106],"to":[4,51,68,73,108],"predict":[5],"the":[6,31,42,56,70,75,85,98,110,113,133],"presence":[7],"of":[8,11,44],"a":[9,14,34,61,64,90,102],"set":[10],"attributes":[12,54,57],"from":[13],"given":[15],"image,":[16],"and":[17,63,112,120,129],"it":[18],"plays":[19],"an":[20],"important":[21],"role":[22],"in":[23],"video":[24],"surveillance":[25],"applications.":[26],"Most":[27],"existing":[28],"works":[29],"model":[30],"task":[32],"as":[33,60,89],"multi-label":[35],"classification":[36],"problem.":[37],"Although":[38],"effective,":[39],"they":[40],"ignore":[41],"existence":[43],"correlations":[45],"among":[46],"attributes.":[47],"In":[48],"this":[49],"work,":[50],"learn":[52],"multiple":[53],"jointly,":[55],"are":[58,79],"modeled":[59,88],"subspace":[62],"dictionary":[65,86,111],"introduced":[67],"represent":[69],"subspace.":[71],"Furthermore,":[72],"extract":[74],"convolutional":[76,99,114],"features":[77],"which":[78,93],"more":[80],"suitable":[81],"for":[82],"prediction,":[84],"network":[91,115],"layer":[92],"learned":[95],"jointly":[96],"with":[97],"network.":[100],"Finally,":[101],"novel":[103],"learning":[104],"algorithm":[105],"proposed":[107,134],"optimize":[109],"corporately.":[116],"Extensive":[117],"experimental":[118],"analyses":[119],"evaluations":[121],"on":[122],"two":[123],"largest":[124],"pedestrian":[125],"benchmarks":[127],"PETA":[128],"PA-100K":[130],"demonstrate":[131],"that":[132],"method":[135],"achieves":[136],"state-of-the-art":[137],"performance.":[138]},"counts_by_year":[],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
