{"id":"https://openalex.org/W3031865458","doi":"https://doi.org/10.1109/tip.2020.3045261","title":"Bi-Directional Exponential Angular Triplet Loss for RGB-Infrared Person Re-Identification","display_name":"Bi-Directional Exponential Angular Triplet Loss for RGB-Infrared Person Re-Identification","publication_year":2020,"publication_date":"2020-12-22","ids":{"openalex":"https://openalex.org/W3031865458","doi":"https://doi.org/10.1109/tip.2020.3045261","mag":"3031865458","pmid":"https://pubmed.ncbi.nlm.nih.gov/33351761"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2020.3045261","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2020.3045261","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":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2006.00878","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Hanrong Ye","orcid":"https://orcid.org/0000-0002-7986-6143"},"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":"Hanrong Ye","raw_affiliation_strings":["Key Laboratory of Machine Perception, Shenzhen Graduate School, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7986-6143","affiliations":[{"raw_affiliation_string":"Key Laboratory of Machine Perception, Shenzhen Graduate School, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hong Liu","orcid":"https://orcid.org/0000-0002-7498-6541"},"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":"Hong Liu","raw_affiliation_strings":["Key Laboratory of Machine Perception, Shenzhen Graduate School, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7498-6541","affiliations":[{"raw_affiliation_string":"Key Laboratory of Machine Perception, Shenzhen Graduate School, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Fanyang Meng","orcid":"https://orcid.org/0000-0001-5725-2178"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fanyang Meng","raw_affiliation_strings":["Peng Cheng Laboratory, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-5725-2178","affiliations":[{"raw_affiliation_string":"Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"last","author":{"id":null,"display_name":"Xia 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":"Xia Li","raw_affiliation_strings":["Key Laboratory of Machine Perception, Shenzhen Graduate School, Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Machine Perception, Shenzhen Graduate School, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.2093,"has_fulltext":false,"cited_by_count":65,"citation_normalized_percentile":{"value":0.95340063,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"30","issue":null,"first_page":"1583","last_page":"1595"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.8515999913215637,"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":0.8515999913215637,"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":0.048900000751018524,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.030799999833106995,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/embedding","display_name":"Embedding","score":0.7861999869346619},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6966000199317932},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.59170001745224},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5511999726295471},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.539900004863739},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.47369998693466187},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45969998836517334},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45260000228881836},{"id":"https://openalex.org/keywords/euclidean-space","display_name":"Euclidean space","score":0.44269999861717224}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7861999869346619},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6966000199317932},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6205000281333923},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.59170001745224},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5562000274658203},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5511999726295471},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.539900004863739},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.47369998693466187},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45969998836517334},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45260000228881836},{"id":"https://openalex.org/C186450821","wikidata":"https://www.wikidata.org/wiki/Q17295","display_name":"Euclidean space","level":2,"score":0.44269999861717224},{"id":"https://openalex.org/C70710897","wikidata":"https://www.wikidata.org/wiki/Q680081","display_name":"Separable space","level":2,"score":0.4366999864578247},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.39739999175071716},{"id":"https://openalex.org/C129782007","wikidata":"https://www.wikidata.org/wiki/Q162886","display_name":"Euclidean geometry","level":2,"score":0.38589999079704285},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3790999948978424},{"id":"https://openalex.org/C198043062","wikidata":"https://www.wikidata.org/wiki/Q180953","display_name":"Metric space","level":2,"score":0.37450000643730164},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3598000109195709},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.31220000982284546},{"id":"https://openalex.org/C13336665","wikidata":"https://www.wikidata.org/wiki/Q125977","display_name":"Vector space","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.30480000376701355},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.29159998893737793},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2797999978065491},{"id":"https://openalex.org/C171558263","wikidata":"https://www.wikidata.org/wiki/Q5406122","display_name":"Euclidean distance matrix","level":3,"score":0.2791000008583069},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2718000113964081},{"id":"https://openalex.org/C151376022","wikidata":"https://www.wikidata.org/wiki/Q168698","display_name":"Exponential function","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C2780069185","wikidata":"https://www.wikidata.org/wiki/Q7977945","display_name":"Equivalence (formal languages)","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tip.2020.3045261","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2020.3045261","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:33351761","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33351761","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},{"id":"pmh:oai:arXiv.org:2006.00878","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2006.00878","pdf_url":"https://arxiv.org/pdf/2006.00878","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2006.00878","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2006.00878","pdf_url":"https://arxiv.org/pdf/2006.00878","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2435089432","display_name":null,"funder_award_id":"U1613209","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":69,"referenced_works":["https://openalex.org/W46454230","https://openalex.org/W1538281814","https://openalex.org/W1563344580","https://openalex.org/W1677182931","https://openalex.org/W1949591461","https://openalex.org/W1967988963","https://openalex.org/W1994623790","https://openalex.org/W2058102599","https://openalex.org/W2068042582","https://openalex.org/W2071207147","https://openalex.org/W2089074647","https://openalex.org/W2106277773","https://openalex.org/W2108598243","https://openalex.org/W2115669554","https://openalex.org/W2161969291","https://openalex.org/W2168356304","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2204750386","https://openalex.org/W2213726222","https://openalex.org/W2235916736","https://openalex.org/W2258844511","https://openalex.org/W2342611082","https://openalex.org/W2346369283","https://openalex.org/W2432402544","https://openalex.org/W2467139031","https://openalex.org/W2520774990","https://openalex.org/W2526833393","https://openalex.org/W2585635281","https://openalex.org/W2586899202","https://openalex.org/W2596603442","https://openalex.org/W2606377603","https://openalex.org/W2777534232","https://openalex.org/W2792373886","https://openalex.org/W2795758732","https://openalex.org/W2808134123","https://openalex.org/W2808260522","https://openalex.org/W2810967842","https://openalex.org/W2890812519","https://openalex.org/W2892034016","https://openalex.org/W2896888563","https://openalex.org/W2898047322","https://openalex.org/W2904973725","https://openalex.org/W2950703532","https://openalex.org/W2954773727","https://openalex.org/W2962926870","https://openalex.org/W2963000559","https://openalex.org/W2963047834","https://openalex.org/W2963289251","https://openalex.org/W2963597983","https://openalex.org/W2963842104","https://openalex.org/W2963910742","https://openalex.org/W2964186374","https://openalex.org/W2964197812","https://openalex.org/W2967515867","https://openalex.org/W6631190155","https://openalex.org/W6638667902","https://openalex.org/W6675751002","https://openalex.org/W6682286002","https://openalex.org/W6688143368","https://openalex.org/W6725558437","https://openalex.org/W6728374919","https://openalex.org/W6730323794","https://openalex.org/W6735531217","https://openalex.org/W6743440100","https://openalex.org/W6748621115","https://openalex.org/W6755310813","https://openalex.org/W6759377594","https://openalex.org/W6767670044"],"related_works":[],"abstract_inverted_index":{"RGB-Infrared":[0],"person":[1],"re-identification":[2],"(RGB-IR":[3],"Re-ID)":[4],"is":[5,14,66,150],"a":[6,15,83,125],"cross-modality":[7],"matching":[8],"problem,":[9],"where":[10],"the":[11,28,53,70,106,118,137,148,167,176,183,197,209],"modality":[12],"discrepancy":[13,29],"big":[16],"challenge.":[17],"Most":[18],"existing":[19],"works":[20],"use":[21],"Euclidean":[22,49],"metric":[23],"based":[24,73],"constraints":[25],"to":[26,94,113,156,175,192,204],"resolve":[27],"between":[30,56,109],"features":[31],"of":[32,42,120,178],"images":[33,72],"from":[34,152,188,200],"different":[35],"modalities.":[36],"However,":[37],"these":[38],"methods":[39],"are":[40],"incapable":[41],"learning":[43],"angularly":[44,62,98],"discriminative":[45,63],"feature":[46,64,101],"embedding":[47,57,76,110,121],"because":[48],"distance":[50],"cannot":[51],"measure":[52],"included":[54,107],"angle":[55],"vectors":[58],"effectively.":[59],"As":[60],"an":[61,97],"space":[65,102,127],"important":[67],"for":[68,160],"classifying":[69],"human":[71],"on":[74,136,196,208],"their":[75],"vectors,":[77,122],"in":[78],"this":[79],"paper,":[80],"we":[81,123],"propose":[82],"novel":[84],"ranking":[85],"loss":[86],"function,":[87],"named":[88],"Bi-directional":[89],"Exponential":[90],"Angular":[91],"Triplet":[92],"Loss,":[93],"help":[95,114],"learn":[96,117],"separable":[99],"common":[100,126],"by":[103],"explicitly":[104],"constraining":[105],"angles":[108],"vectors.":[111],"Moreover,":[112],"stabilize":[115],"and":[116,133,139,181],"magnitudes":[119],"adopt":[124],"batch":[128],"normalization":[129],"layer.":[130],"The":[131,169],"quantitative":[132],"qualitative":[134],"experiments":[135],"SYSU-MM01":[138,146],"RegDB":[140],"dataset":[141],"support":[142],"our":[143],"analysis.":[144],"On":[145],"dataset,":[147,199],"performance":[149],"improved":[151],"7.40%":[153],"/":[154,158,163,186,190,194,202,206],"11.46%":[155],"38.57%":[157],"38.61%":[159],"rank-1":[161,184],"accuracy":[162,185],"mAP":[164,187],"compared":[165],"with":[166],"baseline.":[168],"proposed":[170],"method":[171],"can":[172],"be":[173],"generalized":[174],"task":[177],"single-modality":[179],"Re-ID":[180],"improves":[182],"92.0%":[189],"81.7%":[191],"94.7%":[193],"86.6%":[195],"Market-1501":[198],"82.6%":[201],"70.6%":[203],"87.6%":[205],"77.1%":[207],"DukeMTMC-reID":[210],"dataset.":[211]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":18},{"year":2022,"cited_by_count":17},{"year":2021,"cited_by_count":8}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2020-06-05T00:00:00"}