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Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution

CVPR 2023 · Video › Video segmentation and tracking · approach: Real-world video super-resolution · 53 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use event-guided spatial-temporal implicit representations for VSROurs · CED ×2PSNR: 37.32 -> 38.69Helped
Use event-guided spatial-temporal implicit representations for VSROurs · CED ×4PSNR: 30.15 -> 31.12Helped
Use event-guided spatial-temporal implicit representations for VSROurs · ALPIX-VSR ×2PSNR: 36.10 -> 38.25Helped
Use event-guided spatial-temporal implicit representations for VSROurs · ALPIX-VSR ×4PSNR: 35.30 -> 37.12Helped
Use event-guided spatial-temporal implicit representations for VSROurs · ALPIX-VSR ×6PSNR: 31.15 -> 31.85Helped
Use event-guided spatial-temporal implicit representations for VSROurs · ALPIX-VSR ×8PSNR: 28.11 -> 28.53Helped
Add the TF branch (ablation)STF/STIR framework · CED ablation subsetPSNR: 37.96 -> 38.14Helped
Add shallow feature fusion (ablation)STF/STIR framework · CED ablation subsetPSNR: 37.76 -> 38.14Helped
Use linear rather than nearest interpolation (ablation)STIR · CED ablation subsetPSNR: 28.91 -> 38.14Helped
Use CNN rather than SIREN decoder (ablation)STIR · CED ablation subsetPSNR: 10.30 -> 38.14Helped
Use CNN rather than MLP decoder (ablation)STIR · CED ablation subsetPSNR: 37.94 -> 38.14Helped
Use 16 rather than 8 LF channels (ablation)STIR · CED ablation subsetPSNR: 36.82 -> 38.14Helped

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