{"id":"https://openalex.org/W7157048938","doi":"https://doi.org/10.48550/arxiv.2604.23281","title":"Contrastive Learning for Multimodal Human Activity Recognition with Limited Labeled Data","display_name":"Contrastive Learning for Multimodal Human Activity Recognition with Limited Labeled Data","publication_year":2026,"publication_date":"2026-04-25","ids":{"openalex":"https://openalex.org/W7157048938","doi":"https://doi.org/10.48550/arxiv.2604.23281"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.23281","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23281","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.2604.23281","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134774992","display_name":"Long Jing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jing, Long","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134782773","display_name":"Zhixiong Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Zhixiong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134796267","display_name":"Yajun Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yajun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5028639093","display_name":"Xinlong Feng","orcid":"https://orcid.org/0000-0002-5354-6424"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Xinlong","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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.6266999840736389,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.6266999840736389,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.31349998712539673,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.006000000052154064,"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/activity-recognition","display_name":"Activity recognition","score":0.7462999820709229},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.536300003528595},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.42089998722076416},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4129999876022339},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41179999709129333},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.38600000739097595},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.37229999899864197},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.35249999165534973}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7793999910354614},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.7462999820709229},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.628600001335144},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.536300003528595},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4381999969482422},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.42089998722076416},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4129999876022339},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41179999709129333},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.38600000739097595},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.37229999899864197},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.35249999165534973},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.3465000092983246},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.3149999976158142},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2660999894142151}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.23281","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23281","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.2604.23281","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23281","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Human":[0],"activity":[1,32,68],"recognition":[2,69,74,163],"serves":[3],"as":[4],"the":[5,87,120],"foundation":[6],"for":[7,66],"various":[8],"emerging":[9],"applications.":[10],"In":[11,55,86,119],"recent":[12],"years,":[13],"researchers":[14],"have":[15],"used":[16],"collaborative":[17,139],"sensing":[18,33],"of":[19],"multi-source":[20],"sensors":[21],"to":[22,95,115],"capture":[23,96],"complex":[24],"and":[25,41,52,103,129,145,165],"dynamic":[26],"human":[27,31,67],"activities.":[28],"However,":[29],"multimodal":[30,73],"typically":[34],"encounters":[35],"highly":[36],"heterogeneous":[37],"data":[38],"across":[39],"modalities":[40],"label":[42],"scarcity,":[43],"resulting":[44],"in":[45,161],"an":[46],"application":[47],"gap":[48],"between":[49],"existing":[50],"solutions":[51],"real-world":[53],"needs.":[54],"this":[56],"paper,":[57],"we":[58],"propose":[59],"CLMM,":[60],"a":[61,81,92,107,123,137],"general":[62],"contrastive":[63],"learning":[64],"framework":[65],"that":[70,155],"achieves":[71],"effective":[72],"with":[75],"limited":[76],"labeled":[77],"data.":[78],"CLMM":[79,90,156],"employs":[80,91],"novel":[82],"two-stage":[83],"training":[84,140],"strategy.":[85],"first":[88],"stage,":[89,122],"CNN-DiffTransformer":[93],"encoder":[94],"cross-modal":[97],"shared":[98,117,144],"information":[99],"by":[100],"extracting":[101],"local":[102],"global":[104],"features.":[105],"Meanwhile,":[106],"hard-positive":[108],"samples":[109],"weighting":[110],"algorithm":[111],"enhances":[112],"gradient":[113],"propagation":[114],"reinforce":[116],"learning.":[118],"second":[121],"dual-branch":[124],"architecture":[125],"combining":[126],"quality-guided":[127],"attention":[128],"bidirectional":[130],"gated":[131],"units":[132],"captures":[133],"modality-specific":[134,146],"information,":[135],"while":[136],"primary-auxiliary":[138],"strategy":[141],"fuses":[142],"both":[143,162],"information.":[147],"Experimental":[148],"results":[149],"on":[150],"three":[151],"public":[152],"datasets":[153],"demonstrate":[154],"significantly":[157],"improves":[158],"state-of-the-art":[159],"baselines":[160],"accuracy":[164],"convergence":[166],"performance.":[167]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-29T00:00:00"}
