  Zero padding is widely used in convolutional neural networks to prevent the
size of feature maps diminishing too fast. However, it has been claimed to
disturb the statistics at the border. As an alternative, we propose a
context-aware (CA) padding approach to extend the image. We reformulate the
padding problem as an image extrapolation problem and illustrate the effects on
the semantic segmentation task. Using context-aware padding, the ResNet-based
segmentation model achieves higher mean Intersection-Over-Union than the
traditional zero padding on the Cityscapes and the dataset of DeepGlobe
satellite imaging challenge. Furthermore, our padding does not bring noticeable
overhead during training and testing.
