DifNet: Semantic Segmentation by Diffusion Networks

Deep Neural Networks (DNNs) have recently shown state of the art performance on semantic segmentation tasks, however they still suffer from the problem of poor boundary localization and spatial fragmented predictions. The difficulties lie in the requirement of making dense predictions from a long path model all at once, since details are hard to keep when data goes through deeper layers. Instead, in this work, we decompose this difficult task into two relative simple sub-tasks: seed detection which is required to predict initial predictions without need of wholeness and preciseness, and similarity estimation which estimates the possibility of any two nodes that belong to the same class without need of knowing which class they are. We use one branch network for one sub-task each, and apply a cascade of random walk operations base on hierarchical semantics to approximate a complex diffusion process which propagates seed information to the whole image according to the estimated similarities. The proposed DifNet consistently produces an improvement over the baseline models with same depth and equivalent number of parameters, and also get promising performance on Pascal VOC 2012 and Pascal Context dataset. Our DifNet is trained end-to-end without complex loss functions.