Split-guidance network for salient object detection.

Due to the large-scale variation in objects in practical scenes, multi-scale representation is of critical importance for salient object detection (SOD). Recent advances in multi-level feature fusion also demonstrate its contribution in consistent performance gains. Different from the existing layer-wise methods, we propose a simple yet efficient split-guidance convolution block to improve the multi-scale representation ability at a granular level in this paper. Specifically, the input feature is first split into different subsets; each of them is guided by all the subsets in front of it, in this way to increase the range of receptive fields for each network layer. By embedding it into each side-output stage of the encoder, we build a unified decoder for both RGB SOD and RGB-D SOD. Experimental results on five RGB datasets, five RGB-D datasets and three RGB-T datasets demonstrate that the proposed method without any attention mechanisms and other complex designs performs favorably against state-of-the-art approaches and also shows advantages in simplicity, efficiency and compactness. [ABSTRACT FROM AUTHOR]