Capsule Based Networks, Next Evolution of Convolutional Neural Networks?

Deep convolutional neural networks (CNNs), assisted by architectural design strategies, make large use of data augmentation techniques and layers with a high number of feature maps to embed object transformations. That is highly inefficient and for large datasets implies a massive redundancy of features detectors. Even though capsule networks are still in their infancy, they constitute a promising solution to extend current convolutional networks and endow artificial visual perception with a process to encode more efficiently all feature affine transformations. Are they going to be the next evolutionary step for CNNs?