*Result*: DC-Net: Divide-and-conquer for salient object detection.

*In this paper, to guide the model's training process to explicitly present a progressive trend, we first introduce the concept of Divide-and-Conquer into Salient Object Detection (SOD) tasks, called DC-Net. Our DC-Net guides multiple encoders to solve different subtasks and then aggregates the feature maps with different semantic information obtained by multiple encoders into the decoder to predict the final saliency map. The decoder of DC-Net consists of newly designed two-level Residual nested-ASPP (ResASPP2) modules, which improve the sparse receptive field existing in ASPP and the disadvantage that the U-shape structure needs downsampling to obtain a large receptive field. Based on the advantage of Divide-and-Conquer's parallel computing, we parallelize DC-Net through reparameterization, achieving competitive performance on five 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. • Divide-and-Conquer for salient object detection. • Parallel versions of ResNet and Swin-Transformer are implemented by reparameterization. • A new multi-scale contextual module improves the disadvantage of ASPP and U-shape structure. • Edge maps with different edge widths can impact the model performance. • The pipeline for high-resolution and high-quality segmentation for the meticulous fields of medical, aviation, and military. [ABSTRACT FROM AUTHOR]*