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.

翻译：本文提出将分而治之策略引入显著目标检测任务，使模型能够学习预测显著性图所需的先验知识。我们设计了一种新型网络——分而治之网络（DC-Net），该网络采用两个编码器分别求解有助于预测最终显著性图的子任务：一是预测宽度为4的边缘图，二是预测显著目标的位置图；随后将具有不同语义信息的特征图汇聚至解码器，以预测最终的显著性图。DC-Net的解码器由我们新设计的双层残差嵌套ASPP模块（ResASPP$^{2}$）构成。该模块能以较少的卷积操作捕获大量不同尺度的特征，同时具备始终保持高分辨率、可获得大而紧凑的有效感受野（ERF）的优势。基于分而治之并行计算的特点，我们采用并行加速技术提升DC-Net的速度，使其在六组低分辨率（LR-SOD）和五组高分辨率（HR-SOD）数据集上以高计算效率（60 FPS和55 FPS）实现具有竞争力的性能。相关代码与结果已公开于：https://github.com/PiggyJerry/DC-Net。