Research map / Video / Video segmentation and tracking
Flexible Spatio-Temporal Networks for Video Prediction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add encoder loss to decoder loss (ablation) | FSTN · PV, first frame prediction | PSNR: 26.4 -> 27.8 | Helped |
| Add encoder loss to decoder loss (ablation) | FSTN · PV, first frame prediction | Sharpness: 0.69 -> 0.89 | Helped |
| Add adversarial loss to decoder and encoder losses (ablation) | FSTN · PV, first frame prediction | PSNR: 27.8 -> 30.6 | Helped |
| Add adversarial loss to decoder and encoder losses (ablation) | FSTN · PV, fourth frame prediction | Sharpness: 0.87 -> 0.98 | Helped |
Closest papers by meaning
- Decomposing Motion and Content for Natural Video Sequence Prediction (ICLR 2017 · similarity 0.89)
- PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs (NeurIPS 2017 · similarity 0.88)
- FASTER Recurrent Networks for Efficient Video Classification (AAAI 2020 · similarity 0.88)
- Motion Graph Unleashed: A Novel Approach to Video Prediction (NeurIPS 2024 · similarity 0.87)
- DFDNet: Disentangling and Filtering Dynamics for Enhanced Video Prediction (AAAI 2025 · similarity 0.87)
- STDiff: Spatio-Temporal Diffusion for Continuous Stochastic Video Prediction (AAAI 2024 · similarity 0.87)
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