Modular Retrieval-Augmented Generalization for Human Action Recognition
arXiv 2605.08117•590314943d3e4d878d377789db6f9113e028017fbbe6525a6618361f6d440942
EEG-denoisingIMURF-inverse-designTMS-EEGUAVarrhythmia-classificationchannel-gain-mappingdata-collectiondomain-adaptationhuman-activity-recognitionmachine-learningmedical-devicesneural-posterior-estimationopen-sourceprivacyradar-soundingremote-sensingretrieval-augmentedsmartphone-EEGstructural-health-monitoringtelemedicinetrajectory-planningtransformerwearable-ECGwireless-security
Paper metadata
- arXiv ID
- 2605.08117
- Version
- Not specified by this published record
- Category
- Electrical Engineering and Systems Science — Signal Processing (eess.SP)
The PDF link points to arxiv.org. Baitaphish does not expose a private stored PDF.
Evidence and limitations
- Source ID
- arxiv_eess_sp
- Record identifier
- 590314943d3e4d878d377789db6f9113e028017fbbe6525a6618361f6d440942
- Enrichment time
- 2026-05-12T08:51:40Z
- AI-assisted enrichment
- Yes
This record may overlap with other records. Its enrichment can be incomplete or wrong, and machine assistance was used. Validate consequential decisions against the linked source and your own environment.