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Recurrent Video Restoration Transformer with Guided Deformable Attention

NeurIPS 2022 · Video › Video segmentation and tracking · approach: Real-world video super-resolution · 81 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
increase clip length from 1 to 2 (ablation)RVRT · REDS (ablation)PSNR (dB): 31.98 -> 32.10Helped
increase clip length from 2 to 3 (ablation)RVRT · REDS (ablation)PSNR (dB): 32.10 -> 32.07No clear effect
replace optical-flow warping with GDA for alignment (ablation)RVRT · REDS (ablation)PSNR (dB): 28.88 -> 32.10Helped
replace deformable convolution with GDA (ablation)RVRT · REDS (ablation)PSNR (dB): 31.93 -> 32.10Helped
align from whole clip instead of frame by frame in GDA (ablation)GDA · REDS (ablation)PSNR (dB): 32.00 -> 32.10Helped
add optical flow guidance to GDA (ablation)GDA · REDS (ablation)PSNR (dB): 30.99 -> 32.03Helped
add optical flow update across layers (ablation)GDA · REDS (ablation)PSNR (dB): 32.03 -> 32.10Helped
add channel-interaction MLP in GDA (ablation)GDA · REDS (ablation)PSNR (dB): 31.83 -> 32.10Helped
increase deformable groups from 1 to 12 (ablation)GDA · REDS (ablation)PSNR (dB): 31.63 -> 32.10Helped
increase attention heads from 12 to 36 (ablation)GDA with 12 groups · REDS (ablation)PSNR (dB): 32.10 -> 32.03Hurt
RVRT instead of VRTvideo SR · REDS4 x4PSNR (dB): 32.19 -> 32.75Helped
RVRT instead of VRTvideo denoising · Set8 sigma=10PSNR (dB): 37.88 -> 37.53Mixed

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