  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.

Computer Vision and Pattern Recognition