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Recurrent Feature Reasoning for Image Inpainting

CVPR 2020 · Architectures and efficiency › CNN design · approach: Dense matching and estimation CNNs · 496 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use recurrent feature reasoning with KCARFR-Net · Places2, CelebA, and Paris Street View, 50%-60% masksSSIM: 0.574 -> 0.596 (Places2); 0.799 -> 0.819 (CelebA); 0.659 -> 0.681 (Paris Street View)Helped
Use recurrent feature reasoning with KCARFR-Net · Places2, CelebA, and Paris Street View, 50%-60% masksPSNR: 18.67 -> 18.92 (Places2); 22.37 -> 22.88 (CelebA); 22.07 -> 22.40 (Paris Street View)Helped
Remove the attention module (ablation)RFR-Net · Paris Street View, 40%-50% maskSSIMHurt
Increase recurrence count from 6 to 7 (ablation)RFR-Net · Paris Street ViewSSIM: 0.857 -> 0.852Hurt
Increase recurrence count from 6 to 8 (ablation)RFR-Net · Paris Street ViewSSIM: 0.857 -> 0.854Hurt
Move RFR deeper, after downsampling twice rather than once (ablation)RFR-Net · Paris StreetView, 30%-40% maskSSIM: 0.860 -> 0.845Hurt
Move RFR deeper, after downsampling three times rather than once (ablation)RFR-Net · Paris StreetView, 30%-40% maskSSIM: 0.860 -> 0.839Hurt
Use adaptive feature merging instead of average merging (ablation)RFR-Net · Paris StreetViewvisual detail qualityHelped
Use intermediate feature maps for adaptive merging instead of only the last feature map (ablation)RFR-Net · Paris StreetViewvisual texture qualityHelped
Replace Edge-Connect's structure generator residual blocks with the RFR module (ablation)Edge-Connect structure generator · Structure estimationvisual structure qualityHelped

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