  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.
