  Recent progress on salient object detection (SOD) mainly benefits from
multi-scale learning, where the high-level and low-level features work
collaboratively in locating salient objects and discovering fine details,
respectively. However, most efforts are devoted to low-level feature learning
by fusing multi-scale features or enhancing boundary representations. In this
paper, we show another direction that improving high-level feature learning is
essential for SOD as well. To verify this, we introduce an
Extremely-Downsampled Network (EDN), which employs an extreme downsampling
technique to effectively learn a global view of the whole image, leading to
accurate salient object localization. A novel Scale-Correlated Pyramid
Convolution (SCPC) is also designed to build an elegant decoder for recovering
object details from the above extreme downsampling. Extensive experiments
demonstrate that EDN achieves \sArt performance with real-time speed. Hence,
this work is expected to spark some new thinking in SOD. The code will be
released.
