Convolution has been the basis of most modern neural networks for computer vision. A convolution kernel is spatial-agnostic and channel-specific. Because of this, it isn't able to adapt to different visual patterns with respect to different spatial locations. Along with location-related problems, the receptive field of convolution creates challenges with regard to capturing long-range spatial interactions.