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CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution

CVPR 2023 · Image generation › Super-resolution · approach: CNN and transformer SR architectures · 93 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use CiaoSR instead of LTE with the same RDN backboneRDN arbitrary-scale SR · Urban100, ×4PSNR (dB): 26.81 -> 27.11Helped
Use CiaoSR instead of LTE with the same RDN backboneRDN arbitrary-scale SR · Urban100, ×8PSNR (dB): 22.88 -> 23.13Helped
Add learnable attention-in-attention in place of MLP-predicted ensemble weights (ablation)CiaoSR with RDN · Urban100, ×4PSNR (dB): 26.69 -> 26.96Helped
Add scale-aware non-local attention (ablation)CiaoSR with RDN · Urban100, ×4PSNR (dB): 26.96 -> 27.11Helped
Add learnable attention-in-attention in place of MLP-predicted ensemble weights (ablation)CiaoSR with RDN · Urban100, ×8PSNR (dB): 22.80 -> 22.98Helped
Add scale-aware non-local attention (ablation)CiaoSR with RDN · Urban100, ×8PSNR (dB): 22.98 -> 23.13Helped
Train across continuous scales instead of discrete scales {2, 3, 4} (ablation)CiaoSR with RDN · Urban100, ×4PSNR (dB): 27.02 -> 27.11Helped
Train across continuous scales instead of scale {4} only (ablation)CiaoSR with RDN · Urban100, ×4PSNR (dB): 26.88 -> 27.11Helped
Synthesize ×12 in one step instead of multiple steps (ablation)CiaoSR with RDN · Urban100, ×12PSNR: 21.28 -> 21.42Helped

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