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：本文引入分而治之策略于显著目标检测任务中，使模型能够学习预测显著性图的先验知识。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解决有助于预测最终显著性图的子任务：预测宽度为4的边缘图与显著目标位置图，随后将携带不同语义信息的特征图聚合至解码器以生成最终显著性图。DC-Net的解码器由我们新设计的两级残差嵌套ASPP（ResASPP$^{2}$）模块构成，这类模块能以较少的卷积操作捕获大量不同尺度的特征，同时兼具全程维持高分辨率、获取大而紧致的有效感受野（ERF）的优势。基于分而治之并行计算的特性，我们采用并行加速技术提升DC-Net处理速度，使其在6个低分辨率显著目标检测（LR-SOD）数据集与5个高分辨率显著目标检测（HR-SOD）数据集上，以60 FPS与55 FPS的高效率取得具有竞争力的性能。代码与结果见：https://github.com/PiggyJerry/DC-Net。