{"id":"https://openalex.org/W7123351894","doi":"https://doi.org/10.1109/mmul.2026.3652838","title":"Transformer-Based Decoupled Modality Feature Learning for Visible-Infrared Person Re-Identification","display_name":"Transformer-Based Decoupled Modality Feature Learning for Visible-Infrared Person Re-Identification","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7123351894","doi":"https://doi.org/10.1109/mmul.2026.3652838"},"language":"en","primary_location":{"id":"doi:10.1109/mmul.2026.3652838","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmul.2026.3652838","pdf_url":null,"source":{"id":"https://openalex.org/S72873717","display_name":"IEEE Multimedia","issn_l":"1070-986X","issn":["1070-986X","1941-0166"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 MultiMedia","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pure.ulster.ac.uk/ws/files/237871609/Transformer-based_Decoupled_Modality_Feature_Learning_for_Visible-Infrared_Person_Re-Identification_1_.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122850918","display_name":"Hu Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hu Lu","raw_affiliation_strings":["Jiangsu University, Jiangsu, China"],"raw_orcid":"https://orcid.org/0000-0003-0350-4055","affiliations":[{"raw_affiliation_string":"Jiangsu University, Jiangsu, China","institution_ids":["https://openalex.org/I115592961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122876817","display_name":"Tingting Qin","orcid":null},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingting Qin","raw_affiliation_strings":["Jiangsu University, Jiangsu, China"],"raw_orcid":"https://orcid.org/0009-0008-7230-4448","affiliations":[{"raw_affiliation_string":"Jiangsu University, Jiangsu, China","institution_ids":["https://openalex.org/I115592961"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yuxin Li","orcid":"https://orcid.org/0009-0009-6262-8139"},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxin Li","raw_affiliation_strings":["Jiangsu University, Jiangsu, China"],"raw_orcid":"https://orcid.org/0009-0009-6262-8139","affiliations":[{"raw_affiliation_string":"Jiangsu University, Jiangsu, China","institution_ids":["https://openalex.org/I115592961"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhansheng Liu","orcid":"https://orcid.org/0000-0001-5514-6523"},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhansheng Liu","raw_affiliation_strings":["Jiangsu University, Jiangsu, China"],"raw_orcid":"https://orcid.org/0000-0001-5514-6523","affiliations":[{"raw_affiliation_string":"Jiangsu University, Jiangsu, China","institution_ids":["https://openalex.org/I115592961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076229317","display_name":"Yingquan Wang","orcid":"https://orcid.org/0000-0002-5623-213X"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingquan Wang","raw_affiliation_strings":["Dalian University of Technology, Liaoning, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dalian University of Technology, Liaoning, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122872701","display_name":"Shengli Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I138801177","display_name":"University of Ulster","ror":"https://ror.org/01yp9g959","country_code":"GB","type":"education","lineage":["https://openalex.org/I138801177"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Shengli Wu","raw_affiliation_strings":["Ulster University, Belfast, U.K"],"raw_orcid":"https://orcid.org/0000-0003-2008-1736","affiliations":[{"raw_affiliation_string":"Ulster University, Belfast, U.K","institution_ids":["https://openalex.org/I138801177"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122882289","display_name":"ShaoHua Wan","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"ShaoHua Wan","raw_affiliation_strings":["University of Electronic Science and Technology of China, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-7013-9081","affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Shenzhen, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05254837,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":"1","first_page":"47","last_page":"59"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.8823999762535095,"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":0.8823999762535095,"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":0.0697999969124794,"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":0.005499999970197678,"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/modality","display_name":"Modality (human\u2013computer interaction)","score":0.7407000064849854},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.6514999866485596},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5514000058174133},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.448199987411499},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4172999858856201},{"id":"https://openalex.org/keywords/identity","display_name":"Identity (music)","score":0.3986999988555908},{"id":"https://openalex.org/keywords/decoupling","display_name":"Decoupling (probability)","score":0.3968000113964081},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.37369999289512634},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.3571999967098236}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8669999837875366},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.7407000064849854},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7290999889373779},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.6514999866485596},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5514000058174133},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5088000297546387},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.448199987411499},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4172999858856201},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.3986999988555908},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.3968000113964081},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.37369999289512634},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3571999967098236},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.31209999322891235},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.31139999628067017},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.2994000017642975},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.26350000500679016},{"id":"https://openalex.org/C52970973","wikidata":"https://www.wikidata.org/wiki/Q2497134","display_name":"Adaptive system","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.2524000108242035}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/mmul.2026.3652838","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmul.2026.3652838","pdf_url":null,"source":{"id":"https://openalex.org/S72873717","display_name":"IEEE Multimedia","issn_l":"1070-986X","issn":["1070-986X","1941-0166"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 MultiMedia","raw_type":"journal-article"},{"id":"pmh:oai:pure.atira.dk:publications/8fe51804-1a05-4c8e-bee0-5f0758abfe8f","is_oa":true,"landing_page_url":"https://pure.ulster.ac.uk/en/publications/8fe51804-1a05-4c8e-bee0-5f0758abfe8f","pdf_url":"https://pure.ulster.ac.uk/ws/files/237871609/Transformer-based_Decoupled_Modality_Feature_Learning_for_Visible-Infrared_Person_Re-Identification_1_.pdf","source":{"id":"https://openalex.org/S4306402454","display_name":"Ulster University Research Portal (Ulster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138801177","host_organization_name":"University of Ulster","host_organization_lineage":["https://openalex.org/I138801177"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Lu, H, Qin, T, Li, Y, Liu, Z, Wang, Y, Wu, S & Wan, S 2026, 'Transformer-based Decoupled Modality Feature Learning for Visible-Infrared Person Re-Identification', IEEE MultiMedia, vol. 33, no. 1, pp. 47-59. https://doi.org/10.1109/mmul.2026.3652838","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:publications/8fe51804-1a05-4c8e-bee0-5f0758abfe8f","is_oa":true,"landing_page_url":"https://pure.ulster.ac.uk/en/publications/8fe51804-1a05-4c8e-bee0-5f0758abfe8f","pdf_url":"https://pure.ulster.ac.uk/ws/files/237871609/Transformer-based_Decoupled_Modality_Feature_Learning_for_Visible-Infrared_Person_Re-Identification_1_.pdf","source":{"id":"https://openalex.org/S4306402454","display_name":"Ulster University Research Portal (Ulster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138801177","host_organization_name":"University of Ulster","host_organization_lineage":["https://openalex.org/I138801177"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Lu, H, Qin, T, Li, Y, Liu, Z, Wang, Y, Wu, S & Wan, S 2026, 'Transformer-based Decoupled Modality Feature Learning for Visible-Infrared Person Re-Identification', IEEE MultiMedia, vol. 33, no. 1, pp. 47-59. https://doi.org/10.1109/mmul.2026.3652838","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7123351894.pdf","grobid_xml":"https://content.openalex.org/works/W7123351894.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Visible-Infrared":[0],"person":[1],"re-identification":[2],"(VI-ReID)":[3],"aims":[4],"to":[5,74,89],"match":[6],"pedestrian":[7],"images":[8],"across":[9],"modalities,":[10],"requiring":[11],"the":[12,44,119,137],"simultaneous":[13],"handling":[14],"of":[15],"intra-":[16],"and":[17,31,66,115,130,140],"cross-modality":[18],"discrepancies.":[19],"Existing":[20],"dual-stream":[21],"networks":[22],"extract":[23],"modality-specific":[24,65,99,114],"features":[25,68,95],"but":[26],"often":[27],"suffer":[28],"from":[29,126],"over-coupling":[30],"insufficient":[32],"shared":[33],"identity":[34,72,128],"modeling.":[35],"Simple":[36],"feature":[37],"fusion":[38],"strategies":[39],"do":[40],"not":[41],"adequately":[42],"address":[43],"modality":[45,77,131],"gap.":[46],"We":[47],"propose":[48],"a":[49,104],"ViT-based":[50],"deep":[51],"learning":[52,86,91,122],"framework,":[53],"termed":[54],"Transformer-based":[55],"Decoupled":[56],"Modality":[57],"Feature":[58],"Learning":[59],"(TDMFL),":[60],"which":[61],"effectively":[62],"learns":[63],"both":[64,127],"modality-shared":[67,94,116],"while":[69,96],"leveraging":[70],"modality-invariant":[71,124],"information":[73],"decouple":[75],"different":[76],"representations.":[78],"Specifically,":[79],"we":[80,102],"first":[81],"introduce":[82],"an":[83],"identity-modality":[84],"decoupling":[85],"strategy":[87],"(IMDL)":[88],"facilitate":[90],"with":[92],"reliable":[93],"preserving":[97],"essential":[98],"information.":[100],"Additionally,":[101],"design":[103],"novel":[105],"Identity-Modality":[106],"Aggregation":[107],"(IMA)":[108],"loss":[109],"function":[110],"that":[111,144],"efficiently":[112],"integrates":[113],"features,":[117],"assisting":[118],"model":[120],"in":[121],"more":[123],"representations":[125],"consistency":[129],"adaptation":[132],"perspectives.":[133],"Extensive":[134],"experiments":[135],"on":[136],"SYSU-MM01,":[138],"RegDB,":[139],"LLCM":[141],"datasets":[142],"demonstrate":[143],"our":[145],"method":[146],"significantly":[147],"outperforms":[148],"existing":[149],"state-of-the-art":[150],"approaches.":[151],"Code:":[152],"<ext-link":[153],"ext-link-type=\"uri\"":[154],"xlink:href=\"https://github.com/hulu88/TDMFL\"":[155],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[156],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">https://github.com/hulu88/TDMFL</ext-link>.":[157]},"counts_by_year":[],"updated_date":"2026-04-08T06:01:36.053099","created_date":"2026-01-14T00:00:00"}
