论文标题
动态特征集成，用于同时检测显着物体，边缘和骨骼
Dynamic Feature Integration for Simultaneous Detection of Salient Object, Edge and Skeleton
论文作者
论文摘要
在本文中，我们在统一的框架内解决了三个低级像素视力问题，包括显着对象分割，边缘检测和骨骼提取。我们首先显示这些任务共享的一些相似之处，然后证明如何利用它们来开发可以端对端训练的统一框架。特别是，我们引入了一个选择性集成模块，该模块允许每个任务根据其自身特征从共享骨干方面的不同级别动态选择功能。此外，我们设计了一个任务自适应的注意模块，旨在根据图像内容先验智能地为不同任务分配信息。为了评估我们提出的网络在这些任务上的性能，我们在多个代表性数据集上进行了详尽的实验。我们将证明，尽管这些任务自然是完全不同的，但是我们的网络可以很好地在所有任务上运行，甚至比当前单点最先进的方法更好。此外，我们还进行了足够的消融分析，以充分了解所提出框架的设计原理。为了促进未来的研究，将发布源代码。
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.
