{"id":"https://openalex.org/W3002885160","doi":"https://doi.org/10.1109/vcip47243.2019.8965776","title":"Saliency-based Deep Multi-level Semantic Feature Fusion for Person Re-identification","display_name":"Saliency-based Deep Multi-level Semantic Feature Fusion for Person Re-identification","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3002885160","doi":"https://doi.org/10.1109/vcip47243.2019.8965776","mag":"3002885160"},"language":"en","primary_location":{"id":"doi:10.1109/vcip47243.2019.8965776","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip47243.2019.8965776","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":"https://openalex.org/A5011778497","display_name":"Yinhao Wang","orcid":"https://orcid.org/0000-0002-0416-9378"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinhao Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101727153","display_name":"Chenggang Li","orcid":"https://orcid.org/0000-0001-8481-5183"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenggang Li","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100877228","display_name":"Qingwen Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingwen Hu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053619472","display_name":"Zhicheng Zhao","orcid":"https://orcid.org/0000-0001-6506-7298"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhicheng Zhao","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101754632","display_name":"Fei Su","orcid":"https://orcid.org/0000-0003-4245-4687"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Su","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"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":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/T10331","display_name":"Video Surveillance and Tracking Methods","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/T11448","display_name":"Face recognition and analysis","score":0.9898999929428101,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9896000027656555,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7877330780029297},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7679870128631592},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7012547254562378},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6568800806999207},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.6505545973777771},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6188787221908569},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5995421409606934},{"id":"https://openalex.org/keywords/optimal-distinctiveness-theory","display_name":"Optimal distinctiveness theory","score":0.5882366895675659},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.45983168482780457},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.44233065843582153},{"id":"https://openalex.org/keywords/semantic-feature","display_name":"Semantic feature","score":0.4382331669330597},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4265408515930176}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7877330780029297},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7679870128631592},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7012547254562378},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6568800806999207},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.6505545973777771},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6188787221908569},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5995421409606934},{"id":"https://openalex.org/C47385372","wikidata":"https://www.wikidata.org/wiki/Q7098943","display_name":"Optimal distinctiveness theory","level":2,"score":0.5882366895675659},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.45983168482780457},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.44233065843582153},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.4382331669330597},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4265408515930176},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vcip47243.2019.8965776","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip47243.2019.8965776","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":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2048110836","https://openalex.org/W2125889200","https://openalex.org/W2584637367","https://openalex.org/W2724213014","https://openalex.org/W2755066373","https://openalex.org/W2768610172","https://openalex.org/W2770739811","https://openalex.org/W2807931652","https://openalex.org/W2902214876","https://openalex.org/W2962926870","https://openalex.org/W2963362748","https://openalex.org/W2963383990","https://openalex.org/W2963842104","https://openalex.org/W2964130064","https://openalex.org/W2964163358","https://openalex.org/W3100927979","https://openalex.org/W6746316091","https://openalex.org/W6747428963","https://openalex.org/W6765877273"],"related_works":["https://openalex.org/W4225552138","https://openalex.org/W2032875729","https://openalex.org/W3046185751","https://openalex.org/W2475308993","https://openalex.org/W2790526747","https://openalex.org/W2477273251","https://openalex.org/W2069575426","https://openalex.org/W4390606538","https://openalex.org/W2095903272","https://openalex.org/W3174142935"],"abstract_inverted_index":{"Person":[0],"re-identification":[1],"(Re-ID)":[2],"is":[3,51,65,79,94],"a":[4,41,54,74,88],"challenging":[5],"problem":[6],"due":[7],"to":[8,67,81,96,101],"external":[9],"environmental":[10],"disturbances":[11],"and":[12,23,26,31,48,117],"significant":[13],"intra-class":[14],"appearance":[15],"variations.":[16],"Discriminative":[17],"person":[18],"features":[19],"should":[20],"cover":[21],"global":[22,70],"partial":[24],"representations,":[25],"accordingly":[27],"can":[28],"describe":[29],"high-level":[30,83],"middle-level":[32],"semantic":[33,45,84],"information":[34],"of":[35,61,105,122],"persons.":[36],"To":[37],"achieve":[38],"this":[39],"goal,":[40],"saliency-based":[42,91],"deep":[43,63],"multi-level":[44],"feature":[46,55,76],"representation":[47],"fusion":[49,56],"algorithm":[50,93],"proposed.":[52],"Firstly,":[53],"scheme":[57],"at":[58],"middle":[59],"layer":[60],"our":[62],"network":[64],"presented":[66],"effectively":[68],"fuse":[69],"CNN":[71],"features.":[72,85,107],"Secondly,":[73],"part-based":[75,106],"extraction":[77],"method":[78,125],"designed":[80],"extract":[82],"In":[86],"addition,":[87],"parameter-free":[89],"multi-scale":[90],"enhancement":[92],"proposed":[95,124],"compute":[97],"patch-level":[98],"saliency":[99],"scores":[100],"enhance":[102],"the":[103,120,123],"distinctiveness":[104],"Experimental":[108],"results":[109],"on":[110],"three":[111],"public":[112],"datasets,":[113],"namely,":[114],"Market-1501,":[115],"DukeMTMC-reID,":[116],"CHUK03,":[118],"demonstrate":[119],"effectiveness":[121],"compared":[126],"with":[127],"state-of-the-art":[128],"Re-ID":[129],"approaches.":[130]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
