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SimVP: Simpler Yet Better Video Prediction

CVPR 2022 · Video › Video segmentation and tracking · approach: Stochastic video prediction · 393 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use a simple end-to-end CNN video predictor with MSE lossvideo prediction · Moving MNISTMSE: 24.4 -> 23.8Helped
Use a simple end-to-end CNN video predictor with MSE lossvideo prediction · Human3.6MSE / 10: 36.9 -> 31.6Helped
Use a simple end-to-end CNN video predictor with MSE lossvideo prediction · TrafficBJMAE: 16.2 -> 16.2No clear effect
Use a simple end-to-end CNN video predictorvideo prediction · Caltech Pedestrian (10 → 1)SSIM: 0.927 -> 0.940Helped
Use a simple end-to-end CNN video predictorvideo prediction · KTH (10 → 40)PSNR: 27.56 -> 32.93Helped
Add spatial UNet shortcut (ablation)SimVP · Moving MNISTMSE: 41.7 -> 41.7Helped
Add temporal UNet shortcut (ablation)SimVP · Moving MNISTMSE: 41.5 -> 41.7No clear effect
Use grouped convolution (4 groups rather than 1) (ablation)SimVP · Moving MNISTMSE: 44.8 -> 41.7Helped
Use group normalization rather than batch normalization (ablation)SimVP · Moving MNISTMSE: 41.0 -> 41.7No clear effect
Use multi-scale kernels rather than a single kernel of size 3 (ablation)SimVP translator · Moving MNISTMSE: 58.9 -> 41.7Helped
Use multi-scale kernels rather than a single kernel of size 5 (ablation)SimVP translator · Moving MNISTMSE: 51.1 -> 41.7Helped
Use multi-scale kernels rather than a single kernel of size 7 (ablation)SimVP translator · Moving MNISTMSE: 49.1 -> 41.7Helped

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