Research map / Video / Video segmentation and tracking
Exploring Spatial-Temporal Multi-Frequency Analysis for High-Fidelity and Temporal-Consistency Video Prediction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use spatial and temporal wavelet analysis (ablation) | Video prediction · KTH 10→20 | PSNR: 27.37 -> 29.85 | Helped |
| Add S-WAM (ablation) | STMFANet · KTH 10→20 | PSNR: 29.13 -> 29.85 | Helped |
| Add T-WAM (ablation) | STMFANet · KTH 10→20 | PSNR: 28.57 -> 29.85 | Helped |
| Use spatial and temporal wavelet analysis (ablation) | Video prediction · BAIR | PSNR: 18.15 -> 21.02 | Helped |
| Add S-WAM (ablation) | STMFANet · BAIR | PSNR: 20.22 -> 21.02 | Helped |
| Add T-WAM (ablation) | STMFANet · BAIR | PSNR: 19.87 -> 21.02 | Helped |
| Use spatial and temporal wavelet analysis (ablation) | Video prediction trained on KITTI · Caltech Pedestrian | PSNR: 26.8 -> 29.1 | Helped |
| Add S-WAM (ablation) | STMFANet trained on KITTI · Caltech Pedestrian | PSNR: 28.6 -> 29.1 | Helped |
| Add T-WAM (ablation) | STMFANet trained on KITTI · Caltech Pedestrian | PSNR: 28.1 -> 29.1 | Helped |
| Use deterministic multi-frequency prediction rather than stochastic generation | Video prediction · KTH | LPIPS: 11.81 vs 8.36 | Hurt |
| Use deterministic multi-frequency prediction rather than stochastic generation | Video prediction · BAIR | LPIPS: 9.36 vs 5.50 | Hurt |
| Use deterministic multi-frequency prediction rather than stochastic generation | Video prediction · BAIR | FVD: 159.6 vs 116.4 | Hurt |
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