Reviews: Learning Affinity via Spatial Propagation Networks

The authors incorporate ideas from image processing into CNNs and show how nonlinear diffusion can be combined with deep learning. This allows to train more accurate post-processing modules for semantic segmentation, that are shown to outperform denseCRF-based post-processing, or recurrent alternatives that rely on more straightforward interpretations of recursive signal filtering, as introduced in [3,16]. The main practical contribution lies in extending the techniques of [3,16]: when these techniques apply recursive filtering, say in the horizontal direction of an image, they pass information along rows in isolation. Instead the method of the authors allows one to propagate information across rows, by rephrasing the originally scalar recursion in terms of vector-matrix products. This is shown to be much more effective than the baseline.