  Superpixels are a useful representation to reduce the complexity of image
data. However, to combine superpixels with convolutional neural networks (CNNs)
in an end-to-end fashion, one requires extra models to generate superpixels and
special operations such as graph convolution. In this paper, we propose a way
to implicitly integrate a superpixel scheme into CNNs, which makes it easy to
use superpixels with CNNs in an end-to-end fashion. Our proposed method
hierarchically groups pixels at downsampling layers and generates superpixels.
Our method can be plugged into many existing architectures without a change in
their feed-forward path because our method does not use superpixels in the
feed-forward path but use them to recover the lost resolution instead of
bilinear upsampling. As a result, our method preserves detailed information
such as object boundaries in the form of superpixels even when the model
contains downsampling layers. We evaluate our method on several tasks such as
semantic segmentation, superpixel segmentation, and monocular depth estimation,
and confirm that it speeds up modern architectures and/or improves their
prediction accuracy in these tasks.
