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Dual-GAN: Joint BVP and Noise Modeling for Remote Physiological Measurement
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| jointly model BVP prediction and noise distribution with Dual-GAN | rPPG HR estimation · UBFC-rPPG | MAE: 1.19 -> 0.44 | Helped |
| jointly model BVP prediction and noise distribution with Dual-GAN | rPPG HR estimation · VIPL-HR | MAE: 5.02 -> 4.93 | Helped |
| jointly model BVP prediction and noise distribution with Dual-GAN | rPPG HR estimation · PURE | MAE: 1.84 -> 0.82 | Helped |
| jointly model BVP prediction and noise distribution with Dual-GAN | rPPG HR estimation · PURE training, UBFC-rPPG testing | MAE: 2.09 -> 0.74 | Helped |
| add Noise-GAN and ROI-AF (ablation) | Dual-GAN · UBFC-rPPG | MAE: 1.59 -> 0.44 | Helped |
| add ROI-AF (ablation) | Dual-GAN without Noise-GAN · UBFC-rPPG | MAE: 1.59 -> 0.84 | Helped |
| add ROI-AF (ablation) | Dual-GAN with Noise-GAN · UBFC-rPPG | MAE: 0.66 -> 0.44 | Helped |
| add Noise-GAN (ablation) | Dual-GAN without ROI-AF · UBFC-rPPG | MAE: 1.59 -> 0.66 | Helped |
| add Noise-GAN (ablation) | Dual-GAN with ROI-AF · UBFC-rPPG | MAE: 0.84 -> 0.44 | Helped |
Closest papers by meaning
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- FactorizePhys: Matrix Factorization for Multidimensional Attention in Remote Physiological Sensing (NeurIPS 2024 · similarity 0.84)
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