In this paper, we propose spatial propagation networks for learning the
affinity matrix for vision tasks. We show that by constructing a row/column
linear propagation model, the spatially varying transformation matrix exactly
constitutes an affinity matrix that models dense, global pairwise relationships
of an image. Specifically, we develop a three-way connection for the linear
propagation model, which (a) formulates a sparse transformation matrix, where
all elements can be the output from a deep CNN, but (b) results in a dense
affinity matrix that effectively models any task-specific pairwise similarity
matrix. Instead of designing the similarity kernels according to image features
of two points, we can directly output all the similarities in a purely
data-driven manner. The spatial propagation network is a generic framework that
can be applied to many affinity-related tasks, including but not limited to
image matting, segmentation and colorization, to name a few. Essentially, the
model can learn semantically-aware affinity values for high-level vision tasks
due to the powerful learning capability of the deep neural network classifier.
We validate the framework on the task of refinement for image segmentation
boundaries. Experiments on the HELEN face parsing and PASCAL VOC-2012 semantic
segmentation tasks show that the spatial propagation network provides a
general, effective and efficient solution for generating high-quality
segmentation results.

本研究提出了一种空间传播网络，用于学习视觉任务的亲和矩阵。通过构建行 / 列线性传播模型，可以精确构建空间变化的转换矩阵，作为精确构建全局对偶关系的亲和矩阵，从而建模图像的密集、全局对偶关系。此网络是一个通用框架，可以应用于包括图像抠图、分割和上色等多个领域。实验证明空间传播网络提供了一种生成高质量分割结果的通用、有效和高效的解决方案。