{"id":"https://openalex.org/W7137906865","doi":"https://doi.org/10.48550/arxiv.2603.14243","title":"BIT: Matching-based Bi-directional Interaction Transformation Network for Visible-Infrared Person Re-Identification","display_name":"BIT: Matching-based Bi-directional Interaction Transformation Network for Visible-Infrared Person Re-Identification","publication_year":2026,"publication_date":"2026-03-15","ids":{"openalex":"https://openalex.org/W7137906865","doi":"https://doi.org/10.48550/arxiv.2603.14243"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.14243","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14243","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.14243","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129748225","display_name":"Haoxuan Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xu, Haoxuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129741913","display_name":"Guanglin Niu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niu, Guanglin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5129748225"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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":0.8307999968528748,"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":0.8307999968528748,"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":0.08370000123977661,"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":0.018400000408291817,"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/feature","display_name":"Feature (linguistics)","score":0.7268000245094299},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6209999918937683},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.5554999709129333},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.5400000214576721},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.49380001425743103},{"id":"https://openalex.org/keywords/semantic-gap","display_name":"Semantic gap","score":0.48539999127388},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44670000672340393},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.43869999051094055}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.733299970626831},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7268000245094299},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6209999918937683},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5580000281333923},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.5554999709129333},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.5400000214576721},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.49380001425743103},{"id":"https://openalex.org/C86034646","wikidata":"https://www.wikidata.org/wiki/Q474311","display_name":"Semantic gap","level":4,"score":0.48539999127388},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44670000672340393},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.43869999051094055},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4318000078201294},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.43130001425743103},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.41130000352859497},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.376800000667572},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3402000069618225},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.32600000500679016},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32100000977516174},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2793999910354614},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.2759999930858612},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2678000032901764},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2630999982357025},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.14243","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14243","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.14243","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14243","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Visible-Infrared":[0],"Person":[1],"Re-Identification":[2],"(VI-ReID)":[3],"is":[4,135],"a":[5,33,71,88],"challenging":[6],"retrieval":[7],"task":[8],"due":[9],"to":[10,24,126,138],"the":[11,40,94,109,116,136,162],"substantial":[12],"modality":[13],"gap":[14,27],"between":[15,45,96],"visible":[16,63,97],"and":[17,42,98,115,122],"infrared":[18,56,99],"images.":[19],"While":[20],"existing":[21],"methods":[22],"attempt":[23],"bridge":[25],"this":[26],"by":[28],"learning":[29],"modality-invariant":[30],"features":[31],"within":[32],"shared":[34],"embedding":[35],"space,":[36],"they":[37],"often":[38,59],"overlook":[39],"complex":[41],"implicit":[43],"correlations":[44],"modalities.":[46],"This":[47],"limitation":[48],"becomes":[49],"more":[50],"severe":[51],"under":[52],"distribution":[53],"shifts,":[54],"where":[55,108],"samples":[57],"are":[58],"far":[60],"fewer":[61],"than":[62],"ones.":[64],"To":[65,130],"address":[66],"these":[67],"challenges,":[68],"we":[69],"propose":[70],"novel":[72],"network":[73],"termed":[74],"Bi-directional":[75],"Interaction":[76],"Transformation":[77],"(BIT).":[78],"Instead":[79],"of":[80],"relying":[81],"on":[82,148],"rigid":[83],"feature":[84,113,120],"alignment,":[85],"BIT":[86,103,134,154],"adopts":[87],"matching-based":[89],"strategy":[90],"that":[91,152],"explicitly":[92],"models":[93],"interaction":[95,143],"image":[100],"pairs.":[101],"Specifically,":[102],"employs":[104],"an":[105],"encoder-decoder":[106],"architecture":[107],"encoder":[110],"extracts":[111],"preliminary":[112],"representations,":[114],"decoder":[117],"performs":[118],"bi-directional":[119],"integration":[121],"query":[123],"aware":[124],"scoring":[125],"enhance":[127],"cross-modality":[128],"correspondence.":[129],"our":[131,153],"best":[132],"knowledge,":[133],"first":[137],"introduce":[139],"such":[140],"pairwise":[141],"matching-driven":[142],"in":[144,161],"VI-ReID.":[145],"Extensive":[146],"experiments":[147],"several":[149],"benchmarks":[150],"demonstrate":[151],"achieves":[155],"state-of-the-art":[156],"performance,":[157],"highlighting":[158],"its":[159],"effectiveness":[160],"VI-ReID":[163],"task.":[164]},"counts_by_year":[],"updated_date":"2026-03-18T06:31:55.123368","created_date":"2026-03-18T00:00:00"}