  Salient object detection (SOD) is a fundamental computer vision task.
Recently, with the revival of deep neural networks, SOD has made great
progresses. However, there still exist two thorny issues that cannot be well
addressed by existing methods, indistinguishable regions and complex
structures. To address these two issues, in this paper we propose a novel deep
network for accurate SOD, named CLASS. First, in order to leverage the
different advantages of low-level and high-level features, we propose a novel
non-local cross-level attention (CLA), which can capture the long-range feature
dependencies to enhance the distinction of complete salient object. Second, a
novel cross-level supervision (CLS) is designed to learn complementary context
for complex structures through pixel-level, region-level and object-level. Then
the fine structures and boundaries of salient objects can be well restored. In
experiments, with the proposed CLA and CLS, our CLASS net. consistently
outperforms 13 state-of-the-art methods on five datasets.
