Recently deep convolutional neural networks have achieved significant success in salient object detection. However, existing state-of-the-art methods require high-end GPUs to achieve real-time performance, which makes them hard to adapt to low-cost or portable devices. Although generic network architectures have been proposed to speed up inference on mobile devices, they are tailored to the task of image classification or semantic segmentation, and struggle to capture intra-channel and inter-channel correlations that are essential for contrast modeling in salient object detection. Motivated by the above observations, we design a new deep learning algorithm for fast salient object detection. The proposed algorithm for the first time achieves competitive accuracy and high inference efficiency simultaneously with a single CPU thread. Specifically, we propose a novel depthwise non-local moudule (DNL), which implicitly models contrast via harvesting intra-channel and inter-channel correlations in a self-attention manner. In addition, we introduce a depthwise non-local network architecture that incorporates both depthwise non-local modules and inverted residual blocks. Experimental results show that our proposed network attains very competitive accuracy on a wide range of salient object detection datasets while achieving state-of-the-art efficiency among all existing deep learning based algorithms.

本文提出了一种新的深度学习算法，能够同时实现竞争性准确性和高推理效率，通过深度可分离非局部模块（DNL）和 DNL 网络架构，隐式建模对比以收获通道内和通道间的相关性。该算法成功地在单个 CPU 线程上实现现实时间性能，成为现有所有基于深度学习的算法中效率最高的算法之一。

使用单线程进行快速显著目标检测的深度非局部模块