{"id":"https://openalex.org/W2936222941","doi":"https://doi.org/10.1109/tip.2019.2910408","title":"Two-Level Attention Network With Multi-Grain Ranking Loss for Vehicle Re-Identification","display_name":"Two-Level Attention Network With Multi-Grain Ranking Loss for Vehicle Re-Identification","publication_year":2019,"publication_date":"2019-04-16","ids":{"openalex":"https://openalex.org/W2936222941","doi":"https://doi.org/10.1109/tip.2019.2910408","mag":"2936222941","pmid":"https://pubmed.ncbi.nlm.nih.gov/30998466"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2019.2910408","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2019.2910408","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/A5085707125","display_name":"Haiyun Guo","orcid":"https://orcid.org/0000-0001-9241-6211"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haiyun Guo","raw_affiliation_strings":["School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027279261","display_name":"Kuan Zhu","orcid":"https://orcid.org/0000-0002-1670-944X"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kuan Zhu","raw_affiliation_strings":["School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110938150","display_name":"Ming Tang","orcid":"https://orcid.org/0000-0003-4976-3095"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Tang","raw_affiliation_strings":["School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058420913","display_name":"Jinqiao Wang","orcid":"https://orcid.org/0000-0002-9118-2780"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinqiao Wang","raw_affiliation_strings":["School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5085707125"],"corresponding_institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":7.8681,"has_fulltext":false,"cited_by_count":118,"citation_normalized_percentile":{"value":0.97938894,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"28","issue":"9","first_page":"4328","last_page":"4338"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9998999834060669,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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":0.8997991681098938},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7365477085113525},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6384461522102356},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.6140927672386169},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.5836905241012573},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5787553191184998},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5494588613510132},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5226262211799622},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5019388198852539},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49178245663642883},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4539962410926819},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4414331316947937},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4411502778530121},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4063681960105896},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.35541194677352905},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13173556327819824}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8997991681098938},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7365477085113525},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6384461522102356},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6140927672386169},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.5836905241012573},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5787553191184998},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5494588613510132},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5226262211799622},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5019388198852539},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49178245663642883},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4539962410926819},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4414331316947937},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4411502778530121},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4063681960105896},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35541194677352905},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13173556327819824},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems 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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2019.2910408","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2019.2910408","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:30998466","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/30998466","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":[{"score":0.6700000166893005,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G5582725067","display_name":null,"funder_award_id":"61772527","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5660916271","display_name":null,"funder_award_id":"61806200","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W603908379","https://openalex.org/W1949591461","https://openalex.org/W1958236864","https://openalex.org/W2014850105","https://openalex.org/W2089074647","https://openalex.org/W2135442311","https://openalex.org/W2147527908","https://openalex.org/W2155893237","https://openalex.org/W2194775991","https://openalex.org/W2300840837","https://openalex.org/W2342611082","https://openalex.org/W2433217581","https://openalex.org/W2460852148","https://openalex.org/W2467139031","https://openalex.org/W2470322391","https://openalex.org/W2475284720","https://openalex.org/W2495961871","https://openalex.org/W2512434173","https://openalex.org/W2519904008","https://openalex.org/W2549858646","https://openalex.org/W2594605677","https://openalex.org/W2737725206","https://openalex.org/W2744859288","https://openalex.org/W2770076563","https://openalex.org/W2776879428","https://openalex.org/W2779954854","https://openalex.org/W2788212895","https://openalex.org/W2799251491","https://openalex.org/W2894016331","https://openalex.org/W2951527505","https://openalex.org/W2963495494","https://openalex.org/W2963750390","https://openalex.org/W2963911037","https://openalex.org/W2963977416","https://openalex.org/W2964163358","https://openalex.org/W2964304299","https://openalex.org/W3124951096","https://openalex.org/W4294557331","https://openalex.org/W6618372016","https://openalex.org/W6638444622","https://openalex.org/W6682137061","https://openalex.org/W6684983439","https://openalex.org/W6726373078","https://openalex.org/W6734514467","https://openalex.org/W6746131018","https://openalex.org/W6748066841","https://openalex.org/W6755604751"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W2110523656","https://openalex.org/W1482209366","https://openalex.org/W2521627374"],"abstract_inverted_index":{"Vehicle":[0],"re-identification":[1],"(re-ID)":[2],"aims":[3],"to":[4,69,109,132,149],"identify":[5],"the":[6,20,32,39,76,99,111,135,152,161,171,178,187,208],"same":[7,33],"vehicle":[8,34,46,77,113,182],"across":[9],"multiple":[10],"non-overlapping":[11],"cameras,":[12],"which":[13],"is":[14,107,184],"rather":[15],"a":[16,57,64,145,192],"challenging":[17,220],"task.":[18,79],"On":[19,38],"one":[21,106,123],"hand,":[22,41],"subtle":[23],"changes":[24],"in":[25],"viewpoint":[26],"and":[27,89,118,199,213,224],"illumination":[28],"condition":[29],"can":[30,93,190],"make":[31],"look":[35,49],"much":[36],"different.":[37],"other":[40],"different":[42,45,174,181],"vehicles,":[43],"even":[44],"models,":[47],"may":[48],"quite":[50],"similar.":[51],"In":[52,141],"this":[53],"paper,":[54],"we":[55,143,214],"propose":[56],"novel":[58],"Two-level":[59],"Attention":[60],"network":[61,83,189],"supervised":[62],"by":[63],"Multi-grain":[65],"Ranking":[66],"loss":[67,148],"(TAMR)":[68],"learn":[70,191],"an":[71,125],"efficient":[72],"feature":[73,193],"embedding":[74],"for":[75],"re-ID":[78],"The":[80,104,121],"two-level":[81],"attention":[82,88,92,127],"consisting":[84],"of":[85,102,155,210],"hard":[86],"part-level":[87],"soft":[90],"pixel-level":[91],"adaptively":[94],"extract":[95],"discriminative":[96,153],"features":[97],"from":[98],"visual":[100],"appearance":[101],"vehicles.":[103],"former":[105],"designed":[108],"localize":[110],"salient":[112],"parts,":[114],"such":[115],"as":[116],"windscreen":[117],"car":[119],"head.":[120],"latter":[122],"gives":[124],"additional":[126],"refinement":[128],"at":[129],"pixel":[130],"level":[131],"focus":[133],"on":[134,218],"distinctive":[136],"characteristics":[137],"within":[138],"each":[139],"part.":[140],"addition,":[142],"present":[144],"multi-grain":[146,162],"ranking":[147],"further":[150],"enhance":[151],"ability":[154],"learned":[156],"features.":[157],"We":[158],"creatively":[159],"take":[160],"relationship":[163],"between":[164,173,180],"vehicles":[165,175],"into":[166],"consideration.":[167],"Thus,":[168],"not":[169],"only":[170],"discrimination":[172,201],"but":[176],"also":[177],"distinction":[179],"models":[183],"constrained.":[185],"Finally,":[186],"proposed":[188],"space,":[194],"where":[195],"both":[196],"intra-class":[197],"compactness":[198],"inter-class":[200],"are":[202],"well":[203],"guaranteed.":[204],"Extensive":[205],"experiments":[206],"demonstrate":[207],"effectiveness":[209],"our":[211],"approach":[212],"achieve":[215],"state-of-the-art":[216],"results":[217],"two":[219],"datasets,":[221],"including":[222],"VehicleID":[223],"Vehicle-1M.":[225]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":18},{"year":2022,"cited_by_count":26},{"year":2021,"cited_by_count":30},{"year":2020,"cited_by_count":17},{"year":2019,"cited_by_count":4}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
