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:
this https URL

本文提出使用分而治之的思想来优化显著目标检测任务，并设计了基于此思想的 DC-Net 神经网络模型，其中包含两个编码器和一个解码器，通过预测边缘地图和显著对象位置地图来预测最终显著性图，并采用两级 ResASPP 模块来捕获不同尺度特征，最终在六个 LR-SOD 和五个 HR-SOD 数据集上取得竞争性的性能。