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NeRV: Neural Representations for Videos

NeurIPS 2021 · Video › Video segmentation and tracking · approach: Neural video representations · 3 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
remove positional encoding of frame index (None vs PE) (ablation)NeRV · UVG ablationPSNR: 24.93 -> 37.26Helped
PixelShuffle upscale instead of bilinear pooling (ablation)NeRV · UVG ablationPSNR: 29.56 -> 37.26Helped
PixelShuffle instead of transpose conv upscale (ablation)NeRV · UVG ablationPSNR: 36.63 -> 37.26Helped
add BatchNorm to NeRV block (ablation)NeRV · UVG ablationPSNR: 37.26 -> 36.71Hurt
add InstanceNorm to NeRV block (ablation)NeRV · UVG ablationPSNR: 37.26 -> 35.5Hurt
GELU instead of ReLU (ablation)NeRV · UVG ablationPSNR: 35.89 -> 37.26Helped
GELU instead of Leaky ReLU / Swish (ablation)NeRV · UVG ablationPSNR: 36.76 -> 37.26Helped
L1+SSIM loss instead of L2 loss (ablation)NeRV · UVG ablationPSNR: 35.64 -> 37.26Helped
SSIM alone vs L1+SSIM (ablation)NeRV · UVG ablationMS-SSIM: 0.971 vs 0.970Mixed
train NeRV-S longer, 300 to 2.4k epochsNeRV-S · Big Buck BunnyPSNR: 32.21 -> 34.86Helped
train NeRV-L longer, 300 to 2.4k epochsNeRV-L · Big Buck BunnyPSNR: 39.75 -> 41.99Helped
NeRV vs SIREN at 12.7M paramsNeRV-L · Big Buck BunnyPSNR: 25.06 -> 41.29Helped

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