Abstract:
Image inpainting is a non-trivial task in computer vision due to multiple possibilities for filling the missing data,
which may be dependent on the global information of the
image. Most of the existing approaches use the attention
mechanism to learn the global context of the image. This
attention mechanism produces semantically plausible but
blurry results because of incapability to capture the global
context. In this paper, we introduce hypergraph convolution
on spatial features to learn the complex relationship among
the data. We introduce a trainable mechanism to connect
nodes using hyperedges for hypergraph convolution. To the
best of our knowledge, hypergraph convolution have never
been used on spatial features for any image-to-image tasks
in computer vision. Further, we introduce gated convolution in the discriminator to enforce local consistency in the
predicted image. The experiments on Places2, CelebA-HQ,
Paris Street View, and Facades datasets, show that our approach achieves state-of-the-art results
