Abstract. Benefit from the quick development of deep learning techniques, salient object detection has achieved remarkable progresses recently. However, there still exists following two major challenges that hinder its application in embedded devices, low resolution output and heavy
model weight. To this end, this paper presents an accurate yet compact
deep network for efficient salient object detection. More specifically, given
a coarse saliency prediction in the deepest layer, we first employ residual
learning to learn side-output residual features for saliency refinement,
which can be achieved with very limited convolutional parameters while
keep accuracy. Secondly, we further propose reverse attention to guide
such side-output residual learning in a top-down manner. By erasing
the current predicted salient regions from side-output features, the network can eventually explore the missing object parts and details which
results in high resolution and accuracy. Experiments on six benchmark
datasets demonstrate that the proposed approach compares favorably
against state-of-the-art methods, and with advantages in terms of simplicity, efficiency (45 FPS) and model size (81 MB).