  CNNs have made a tremendous impact on the field of computer vision in the
last several years. The main component of any CNN architecture is the
convolution operation, which is translation invariant by design. However,
location in itself can be an important cue. For example, a salient object is
more likely to be closer to the center of the image, the sky in the top part of
an image, etc. To include the location cue for feature learning, we propose to
augment the color image, the usual input to CNNs, with one or more channels
that carry location information. We test two approaches for adding location
information. In the first approach, we incorporate location directly, by
including the row and column indexes as two additional channels to the input
image. In the second approach, we add location less directly by adding distance
transform from the center pixel as an additional channel to the input image. We
perform experiments with both direct and indirect ways to encode location. We
show the advantage of augmenting the standard color input with location related
channels on the tasks of salient object segmentation, semantic segmentation,
and scene parsing.
