In this paper, we solve three low-level pixel-wise vision problems, including
salient object segmentation, edge detection, and skeleton extraction, within a
unified framework. We first show some similarities shared by these tasks and
then demonstrate how they can be leveraged for developing a unified framework
that can be trained end-to-end. In particular, we introduce a selective
integration module that allows each task to dynamically choose features at
different levels from the shared backbone based on its own characteristics.
Furthermore, we design a task-adaptive attention module, aiming at
intelligently allocating information for different tasks according to the image
content priors. To evaluate the performance of our proposed network on these
tasks, we conduct exhaustive experiments on multiple representative datasets.
We will show that though these tasks are naturally quite different, our network
can work well on all of them and even perform better than current
single-purpose state-of-the-art methods. In addition, we also conduct adequate
ablation analyses that provide a full understanding of the design principles of
the proposed framework. To facilitate future research, source code will be
released.

本文提出了一种基于统一框架的低级像素视觉问题解决方法，通过选择性整合模块和任务自适应注意力模块实现，经实验验证不同的任务可以在同一个框架下进行训练，并在多个代表性数据集上证明了该方法的有效性。