Location Augmentation for CNN
This addresses the limitation of translation invariance in CNNs for tasks where location is important, such as object detection and scene understanding, but it is incremental as it builds on existing CNN architectures.
The authors tackled the problem of incorporating location cues into CNNs for computer vision tasks by augmenting input images with location channels, such as row/column indexes or distance transforms, and demonstrated advantages in salient object segmentation, semantic segmentation, and scene parsing.
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.