  In this paper, we introduce Divide-and-Conquer into the salient object
detection (SOD) task to enable the model to learn prior knowledge that is for
predicting the saliency map. We design a novel network, Divide-and-Conquer
Network (DC-Net) which uses two encoders to solve different subtasks that are
conducive to predicting the final saliency map, here is to predict the edge
maps with width 4 and location maps of salient objects and then aggregate the
feature maps with different semantic information into the decoder to predict
the final saliency map. The decoder of DC-Net consists of our newly designed
two-level Residual nested-ASPP (ResASPP$^{2}$) modules, which have the ability
to capture a large number of different scale features with a small number of
convolution operations and have the advantages of maintaining high resolution
all the time and being able to obtain a large and compact effective receptive
field (ERF). Based on the advantage of Divide-and-Conquer's parallel computing,
we use Parallel Acceleration to speed up DC-Net, allowing it to achieve
competitive performance on six LR-SOD and five HR-SOD datasets under high
efficiency (60 FPS and 55 FPS). Codes and results are available:
https://github.com/PiggyJerry/DC-Net.
