How ‘Copy-and-Paste’ is embedded in CNNs for Image Inpainting — Review: Shift-Net: Image Inpainting via Deep Feature Rearrangement

By Medium - 2020-10-16

Description

Hello everyone:) Welcome back!! Today, we will dive into a more specific deep image inpainting technique, Deep Feature Rearrangement. This technique takes both the advantages of using modern…

Summary

  • Today, we will dive into a more specific deep image inpainting technique, Deep Feature Rearrangement.
  • This method offers good local details as we directly paste other image patches on the missing parts.
  • Contributions As mentioned, a shift-connection layer is proposed to embed the concept of copy-and-paste in modern CNNs such that their proposed model can offer inpainting results with both correct global semantic structure and fine detailed textures.
  • from their paper [1] Recall Figure 2 for the architecture of Shift-Net, 3 different feature maps are concatenated after the shift-connection layer, namely ϕ_L-l(I), ϕ_l(I) and ϕ^shift_L-l(I).

 

Topics

  1. Machine_Learning (0.49)
  2. Backend (0.14)
  3. NLP (0.12)

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