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Bridging Global Context Interactions for High-Fidelity Image Completion

CVPR 2022 · Architectures and efficiency › Attention and transformers · approach: Transformer expressivity and circuits · 118 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use TFill instead of ICTImage completion · Places2 free-form masks 20-50%LPIPS: 0.1045 -> 0.0918; 0.1563 -> 0.1328; 0.1974 -> 0.1796Helped
Use TFill instead of ICTImage completion · Places2 free-form masks 20-50%FID: 17.13 -> 15.28; 22.39 -> 19.99; 28.18 -> 25.88Helped
Use TFill instead of PICImage completion · CelebA-HQ and FFHQ center masksLPIPS: 0.061 -> 0.048; 0.068 -> 0.053Helped
Add attention in G to the Traditional Conv baseline (ablation)TFill architecture · CelebA-HQ center maskFID: 6.29 -> 6.34Hurt
Add a restrictive CNN token embedding (ablation)TFill architecture · CelebA-HQ center maskFID: 6.34 -> 4.68Helped
Add a transformer encoder (ablation)TFill architecture · CelebA-HQ center maskFID: 4.68 -> 4.02Helped
Add masked weights to transformer attention (ablation)TFill architecture · CelebA-HQ center maskFID: 4.02 -> 3.92Helped
Add the refinement network (ablation)TFill architecture · CelebA-HQ center maskFID: 3.92 -> 3.86Helped
Add a restrictive CNN token embedding (ablation)TFill architecture · FFHQ center maskFID: 4.01 -> 3.87Helped
Add a transformer encoder (ablation)TFill architecture · FFHQ center maskFID: 3.87 -> 3.66Helped
Add masked weights to transformer attention (ablation)TFill architecture · FFHQ center maskFID: 3.66 -> 3.63Helped
Add the refinement network (ablation)TFill architecture · FFHQ center maskFID: 3.63 -> 3.50Helped

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