{"id":"https://openalex.org/W7166544667","doi":"https://doi.org/10.48550/arxiv.2606.28089","title":"RPM-Distill: Physiology-guided Adaptive Cross-modal Distillation for Robust Remote Physiological Measurement","display_name":"RPM-Distill: Physiology-guided Adaptive Cross-modal Distillation for Robust Remote Physiological Measurement","publication_year":2026,"publication_date":"2026-06-26","ids":{"openalex":"https://openalex.org/W7166544667","doi":"https://doi.org/10.48550/arxiv.2606.28089"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.28089","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28089","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.28089","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139572302","display_name":"Jiyao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jiyao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139586517","display_name":"Qingyong Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Qingyong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022235180","display_name":"Da Tang","orcid":"https://orcid.org/0000-0001-7178-0962"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Duoxun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030018760","display_name":"Xiaomin Yang","orcid":"https://orcid.org/0000-0002-5593-5108"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Xiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139584278","display_name":"Kaishun Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Kaishun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5107877612","display_name":"J. Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Jiangbo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9807999730110168,"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"}},"topics":[{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9807999730110168,"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"}},{"id":"https://openalex.org/T11932","display_name":"Wireless Body Area Networks","score":0.002300000051036477,"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"}},{"id":"https://openalex.org/T11778","display_name":"Electrical and Bioimpedance Tomography","score":0.0013000000035390258,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/radar","display_name":"Radar","score":0.6079999804496765},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5533000230789185},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.5282999873161316},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5081999897956848},{"id":"https://openalex.org/keywords/cyclostationary-process","display_name":"Cyclostationary process","score":0.49149999022483826},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4690000116825104},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.39430001378059387},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.36059999465942383}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6280999779701233},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.6079999804496765},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5533000230789185},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.5282999873161316},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5081999897956848},{"id":"https://openalex.org/C178351263","wikidata":"https://www.wikidata.org/wiki/Q3922399","display_name":"Cyclostationary process","level":3,"score":0.49149999022483826},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4690000116825104},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4575999975204468},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.39430001378059387},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.36059999465942383},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.35569998621940613},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.323199987411499},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30390000343322754},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.28780001401901245},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.28519999980926514},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.28369998931884766},{"id":"https://openalex.org/C2984296375","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar systems","level":3,"score":0.28209999203681946},{"id":"https://openalex.org/C2985906921","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Spectral properties","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.27300000190734863},{"id":"https://openalex.org/C176641082","wikidata":"https://www.wikidata.org/wiki/Q2446767","display_name":"Spectral signature","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C74064498","wikidata":"https://www.wikidata.org/wiki/Q3396184","display_name":"Radio frequency","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C108597893","wikidata":"https://www.wikidata.org/wiki/Q663650","display_name":"Reflectivity","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26339998841285706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.28089","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28089","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.28089","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28089","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":"Preprint"},"sustainable_development_goals":[{"score":0.722087562084198,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Video-based":[0],"remote":[1],"physiological":[2],"measurement":[3],"(RPM)":[4],"is":[5,22,39,72,187],"highly":[6],"accessible":[7],"but":[8],"remains":[9],"fragile":[10],"under":[11,130],"varying":[12],"illumination,":[13],"skin":[14],"tones,":[15],"and":[16,27,47,76,83,119,124,134,145,172,179],"motion.":[17],"Radio":[18],"frequency":[19,94],"(RF)":[20],"radar":[21,36,61],"largely":[23],"invariant":[24],"to":[25,43,102],"illumination":[26],"appearance,":[28],"providing":[29],"complementary":[30],"cardio-respiratory":[31],"micro-motion":[32],"cues;":[33],"however,":[34],"requiring":[35],"at":[37,188],"inference":[38],"often":[40],"impractical":[41],"due":[42],"its":[44],"limited":[45],"ubiquity":[46],"deployment":[48],"overhead.":[49],"We":[50,96],"propose":[51],"RPM-Distill,":[52],"a":[53,137,156,161],"physiology-guided":[54],"cross-modal":[55],"distillation":[56,143],"framework":[57],"that":[58,73,107],"leverages":[59],"synchronized":[60],"only":[62],"during":[63],"training":[64],"while":[65],"retaining":[66],"video-only":[67],"inference.":[68],"Our":[69],"key":[70],"observation":[71],"although":[74],"RGB":[75],"RF":[77],"waveforms":[78],"differ":[79],"in":[80,92,169],"sensing":[81],"physics":[82],"time-domain":[84],"morphology,":[85],"they":[86],"share":[87],"similar":[88],"latent":[89],"periodic":[90],"rhythm":[91],"the":[93,110,115,149],"domain.":[95],"thus":[97],"distill":[98],"physiology-structured":[99],"spectral":[100,122,138,151],"evidence":[101],"improve":[103],"robustness,":[104],"via":[105],"losses":[106],"(i)":[108],"anchor":[109],"fundamental":[111],"peak,":[112],"(ii)":[113],"match":[114],"off-peak":[116],"background":[117],"distribution,":[118],"(iii)":[120],"preserve":[121],"morphology":[123],"sharpness.":[125],"To":[126],"avoid":[127],"negative":[128],"transfer":[129],"sample-level":[131,142],"teacher":[132],"quality":[133],"alignment":[135],"uncertainty,":[136],"policy":[139],"network":[140],"predicts":[141],"gates":[144],"component":[146],"weights":[147],"from":[148],"student--teacher":[150],"relation":[152],"map,":[153],"learned":[154],"with":[155],"meta":[157],"bilevel":[158],"objective":[159],"on":[160],"small":[162],"labeled":[163],"validation":[164],"split.":[165],"Through":[166],"extensive":[167],"experiments":[168],"challenging":[170],"conditions":[171],"cross-dataset":[173],"settings,":[174],"RPM-Distill":[175],"brings":[176],"81\\%":[177],"MAE":[178],"21\\%":[180],"correlation":[181],"improvement":[182],"over":[183],"unimodal":[184],"baselines.":[185],"Code":[186],"https://github.com/WJULYW/RPM-Distill.":[189]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-30T00:00:00"}