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ResearchFormerly WizWand

Video Prediction on Moving-MNIST 10 → 10 (test)

15.05MSESimVPv2-S×10
11.37636.175560.97585.7745Apr 2, 2021Jul 10, 2021Oct 18, 2021Jan 26, 2022May 6, 2022Aug 14, 2022Nov 22, 2022
Updated 8mo ago

Evaluation results

MethodLinks
SimVPv2-S×10FLOPs (G)=16.53, Training time (total s)=1560, Inference efficiency (FPS)=44.09, Training Epochs=20002022.1115.050.967—49.8
SimVPv2-S×5FLOPs (G)=16.53, Training time (total s)=780, Inference efficiency (FPS)=44.09, Training Epochs=10002022.1116.470.964—53.24
Temporal Attention Unit2022.0619.80.957—60.3
SimVPv2-LFLOPs (G)=152.2, Training time (s/epoch)=796, Inference efficiency (FPS)=21.232022.1121.810.952—66.43
Crevnet2022.0622.30.949——
SimVPv2-S×3FLOPs (G)=16.53, Training time (total s)=468, Inference efficiency (FPS)=44.09, Training Epochs=6002022.1122.370.951—67.52
PredRNN++FLOPs (G)=171.73, Training time (s/epoch)=1280, Inference efficiency (FPS)=3.712022.1122.450.95—69.7
MIMFLOPs (G)=179.18, Training time (s/epoch)=1388, Inference efficiency (FPS)=3.082022.1123.660.946—74.37
SimVP2022.0623.80.948—68.9
PhyDNet2022.0624.40.947—70.3
PredRNNFLOPs (G)=115.95, Training time (s/epoch)=869, Inference efficiency (FPS)=3.972022.1125.040.944—76.26
SimVPv2-SFLOPs (G)=16.53, Training time (s/epoch)=156, Inference efficiency (FPS)=44.092022.1126.60.94—77.32
SwinLSTMFLOPs (G)=69.87, Training time (s/epoch)=820, Inference efficiency (FPS)=6.512022.1127.440.938—78.69
PredRNNv2FLOPs (G)=116.59, Training time (s/epoch)=899, Inference efficiency (FPS)=3.492022.1127.730.937—82.17
ConvLSTM-LFLOPs (G)=127.01, Training time (s/epoch)=879, Inference efficiency (FPS)=6.242022.1129.880.925—95.05
CrevNetFLOPs (G)=270.68, Training time (s/epoch)=1166, Inference efficiency (FPS)=1.012022.1130.150.935—86.28
MAUFLOPs (G)=17.79, Training time (s/epoch)=535, Inference efficiency (FPS)=3.082022.1130.640.928—88.17
SimVPFLOPs (G)=19.43, Training time (s/epoch)=261, Inference efficiency (FPS)=27.152022.1132.220.927—89.19
MMVPFLOPs (G)=93.55, Training time (s/epoch)=402, Inference efficiency (FPS)=21.332022.1133.290.926—89.61
PhyDNetFLOPs (G)=15.33, Training time (s/epoch)=452, Inference efficiency (FPS)=4.622022.1135.680.917—96.7
E3D-LSTMFLOPs (G)=298.87, Training time (s/epoch)=2693, Inference efficiency (FPS)=3.732022.1136.190.932—78.64
DDPAE2022.0638.90.922—90.7
E3D-LSTM2022.0641.30.91—87.2
LMC-Memory2021.0441.50.92446.9—
LMC2022.0641.50.924——
PredRNN++2021.0442.10.91359.5—
MIM2022.0644.20.91—101.1
ConvLSTM-SFLOPs (G)=14.45, Training time (s/epoch)=190, Inference efficiency (FPS)=7.52022.1146.260.878—142.18
PredRNN++2022.0646.50.898—106.8
E3D-LSTM2021.0450.90.91286.7—
Conv-TT-LSTM2021.04530.91540.5—
Conv-TT-LSTM2022.06530.915——
PredRNN2021.0456.80.867——
PredRNN2022.0656.80.867—126.1
VPN2021.0464.10.87——
VPN2022.0664.10.87——
CDNA2021.0497.40.721——
ConvLSTM2022.06103.30.707—182.9
TRAJGRU2021.04106.90.713——