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Models/SV2P time-invariant (from Grid-keypoints)

SV2P time-invariant (from Grid-keypoints)

Reported on 16 benchmarks across 2 tasks · 1 paper · 8 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision16 results

VideoonKTH
Params (M)· 2017-10-30
8.3
best: 22.8 (SVG-LP (from Grid-keypoints))
SOTA
Stochastic Variational Video Prediction arXiv:1710.11252
VideoonKTH
Pred· 2017-10-30
40
SOTA
Stochastic Variational Video Prediction arXiv:1710.11252
VideoonKTH
Train· 2017-10-30
10
SOTA
Stochastic Variational Video Prediction arXiv:1710.11252
VideoonKTH
FVD· 2017-10-30
253.5
best: 395 (Struct-VRNN (from Grid-keypoints))
SOTA
Stochastic Variational Video Prediction arXiv:1710.11252
VideoonKTH
Cond· 2017-10-30
10
Stochastic Variational Video Prediction arXiv:1710.11252
VideoonKTH
FVD· 2017-10-30
209.5
best: 395 (Struct-VRNN (from Grid-keypoints))
Stochastic Variational Video Prediction arXiv:1710.11252
VideoonKTH
LPIPS· 2017-10-30
0.232
best: 0.029 (MSPred)
Stochastic Variational Video Prediction arXiv:1710.11252
VideoonKTH
PSNR· 2017-10-30
25.87
best: 29.85 (WAM)
Stochastic Variational Video Prediction arXiv:1710.11252
VideoonKTH
SSIM· 2017-10-30
0.782
best: 0.951 (MSPred)
Stochastic Variational Video Prediction arXiv:1710.11252
VideoonKTH
Cond· 2017-10-30
10
Stochastic Variational Video Prediction arXiv:1710.11252
VideoonKTH
LPIPS· 2017-10-30
0.26
best: 0.029 (MSPred)
Stochastic Variational Video Prediction arXiv:1710.11252
VideoonKTH
PSNR· 2017-10-30
25.7
best: 29.85 (WAM)
Stochastic Variational Video Prediction arXiv:1710.11252
VideoonKTH
Params (M)· 2017-10-30
8.3
best: 22.8 (SVG-LP (from Grid-keypoints))
Stochastic Variational Video Prediction arXiv:1710.11252
VideoonKTH
Pred· 2017-10-30
40
Stochastic Variational Video Prediction arXiv:1710.11252
VideoonKTH
SSIM· 2017-10-30
0.772
best: 0.951 (MSPred)
Stochastic Variational Video Prediction arXiv:1710.11252
VideoonKTH
Train· 2017-10-30
10
Stochastic Variational Video Prediction arXiv:1710.11252

Time Series16 results

Video PredictiononKTH
Params (M)· 2017-10-30
8.3
best: 22.8 (SVG-LP (from Grid-keypoints))
SOTA
Stochastic Variational Video Prediction arXiv:1710.11252
Video PredictiononKTH
Pred· 2017-10-30
40
SOTA
Stochastic Variational Video Prediction arXiv:1710.11252
Video PredictiononKTH
Train· 2017-10-30
10
SOTA
Stochastic Variational Video Prediction arXiv:1710.11252
Video PredictiononKTH
FVD· 2017-10-30
253.5
best: 395 (Struct-VRNN (from Grid-keypoints))
SOTA
Stochastic Variational Video Prediction arXiv:1710.11252
Video PredictiononKTH
Cond· 2017-10-30
10
Stochastic Variational Video Prediction arXiv:1710.11252
Video PredictiononKTH
FVD· 2017-10-30
209.5
best: 395 (Struct-VRNN (from Grid-keypoints))
Stochastic Variational Video Prediction arXiv:1710.11252
Video PredictiononKTH
LPIPS· 2017-10-30
0.232
best: 0.029 (MSPred)
Stochastic Variational Video Prediction arXiv:1710.11252
Video PredictiononKTH
PSNR· 2017-10-30
25.87
best: 29.85 (WAM)
Stochastic Variational Video Prediction arXiv:1710.11252
Video PredictiononKTH
SSIM· 2017-10-30
0.782
best: 0.951 (MSPred)
Stochastic Variational Video Prediction arXiv:1710.11252
Video PredictiononKTH
Cond· 2017-10-30
10
Stochastic Variational Video Prediction arXiv:1710.11252
Video PredictiononKTH
LPIPS· 2017-10-30
0.26
best: 0.029 (MSPred)
Stochastic Variational Video Prediction arXiv:1710.11252
Video PredictiononKTH
PSNR· 2017-10-30
25.7
best: 29.85 (WAM)
Stochastic Variational Video Prediction arXiv:1710.11252
Video PredictiononKTH
Params (M)· 2017-10-30
8.3
best: 22.8 (SVG-LP (from Grid-keypoints))
Stochastic Variational Video Prediction arXiv:1710.11252
Video PredictiononKTH
Pred· 2017-10-30
40
Stochastic Variational Video Prediction arXiv:1710.11252
Video PredictiononKTH
SSIM· 2017-10-30
0.772
best: 0.951 (MSPred)
Stochastic Variational Video Prediction arXiv:1710.11252
Video PredictiononKTH
Train· 2017-10-30
10
Stochastic Variational Video Prediction arXiv:1710.11252