{"id":"https://openalex.org/W2756261853","doi":"https://doi.org/10.1109/icip.2017.8296310","title":"Deep joint discriminative learning for vehicle re-identification and retrieval","display_name":"Deep joint discriminative learning for vehicle re-identification and retrieval","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2756261853","doi":"https://doi.org/10.1109/icip.2017.8296310","mag":"2756261853"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2017.8296310","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296310","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-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/A5030616168","display_name":"Yuqi Li","orcid":"https://orcid.org/0000-0003-0228-242X"},"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":true,"raw_author_name":"Yuqi Li","raw_affiliation_strings":["Peking University, Beijing, P.R. China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, P.R. China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029760000","display_name":"Yanghao Li","orcid":"https://orcid.org/0000-0002-5274-1367"},"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":"Yanghao Li","raw_affiliation_strings":["Peking University, Beijing, P.R. China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, P.R. China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111726041","display_name":"Hongfei Yan","orcid":"https://orcid.org/0000-0001-5914-8585"},"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":"Hongfei Yan","raw_affiliation_strings":["Peking University, Beijing, P.R. China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, P.R. China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100761525","display_name":"Jiaying Liu","orcid":"https://orcid.org/0000-0002-0468-9576"},"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":"Jiaying Liu","raw_affiliation_strings":["Peking University, Beijing, P.R. China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, P.R. China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5030616168"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":2.5486,"has_fulltext":false,"cited_by_count":65,"citation_normalized_percentile":{"value":0.94094084,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9995999932289124,"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":0.9983000159263611,"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/discriminative-model","display_name":"Discriminative model","score":0.9620004892349243},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.817626953125},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6965799331665039},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.6779943108558655},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6397519111633301},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5939059853553772},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5783811807632446},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.5782572031021118},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5378074049949646},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4345913529396057},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4239831566810608},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.0987263023853302}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.9620004892349243},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.817626953125},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6965799331665039},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.6779943108558655},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6397519111633301},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5939059853553772},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5783811807632446},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.5782572031021118},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5378074049949646},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4345913529396057},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4239831566810608},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0987263023853302},{"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/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2017.8296310","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296310","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7400000095367432,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W196211074","https://openalex.org/W1627400044","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W1950843348","https://openalex.org/W1971955426","https://openalex.org/W1998808035","https://openalex.org/W2029315852","https://openalex.org/W2096733369","https://openalex.org/W2144172034","https://openalex.org/W2186615578","https://openalex.org/W2194775991","https://openalex.org/W2470322391","https://openalex.org/W2502225121","https://openalex.org/W2549858646","https://openalex.org/W2953350812","https://openalex.org/W2962835968","https://openalex.org/W3099206234","https://openalex.org/W6607973782","https://openalex.org/W6636759986","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6649863312","https://openalex.org/W6681239517","https://openalex.org/W6686509673"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,43],"propose":[4],"a":[5,12,21,45,69,79],"novel":[6],"vehicle":[7,31,102],"re-identification":[8,103],"method":[9,91],"based":[10],"on":[11,78,100],"Deep":[13],"Joint":[14],"Discriminative":[15],"Learning":[16],"(DJDL)":[17],"model,":[18],"which":[19],"utilizes":[20],"deep":[22],"convolutional":[23],"network":[24,64],"to":[25,48],"effectively":[26],"extract":[27],"discriminative":[28],"representations":[29],"for":[30],"images.":[32],"To":[33],"exploit":[34],"properties":[35],"and":[36,59,92,104],"relationship":[37],"among":[38],"samples":[39],"in":[40],"different":[41,51],"views,":[42],"design":[44],"unified":[46],"framework":[47],"combine":[49],"several":[50],"tasks":[52],"efficiently,":[53],"including":[54],"identification,":[55],"attribute":[56],"recognition,":[57],"verification":[58],"triplet":[60],"tasks.":[61],"The":[62],"whole":[63],"is":[65],"optimized":[66],"jointly":[67],"via":[68],"specific":[70],"batch":[71],"composition":[72],"design.":[73],"Extensive":[74],"experiments":[75],"are":[76],"conducted":[77],"large-scale":[80],"VehicleID":[81],"[1]":[82],"dataset.":[83],"Experimental":[84],"results":[85],"demonstrate":[86],"the":[87,97],"effectiveness":[88],"of":[89],"our":[90],"show":[93],"that":[94],"it":[95],"achieves":[96],"state-of-the-art":[98],"performance":[99],"both":[101],"retrieval.":[105]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":15},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":16},{"year":2018,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
