  Convolutional Neural Networks have revolutionized vision applications. There
are image domains and representations, however, that cannot be handled by
standard CNNs (e.g., spherical images, superpixels). Such data are usually
processed using networks and algorithms specialized for each type. In this
work, we show that it may not always be necessary to use specialized neural
networks to operate on such spaces. Instead, we introduce a new structured
graph convolution operator that can copy 2D convolution weights, transferring
the capabilities of already trained traditional CNNs to our new graph network.
This network can then operate on any data that can be represented as a
positional graph. By converting non-rectilinear data to a graph, we can apply
these convolutions on these irregular image domains without requiring training
on large domain-specific datasets. Results of transferring pre-trained image
networks for segmentation, stylization, and depth prediction are demonstrated
for a variety of such data forms.
