Most salient object detection approaches use U-Net or feature pyramid
networks (FPN) as their basic structures. These methods ignore two key problems
when the encoder exchanges information with the decoder: one is the lack of
interference control between them, the other is without considering the
disparity of the contributions of different encoder blocks. In this work, we
propose a simple gated network (GateNet) to solve both issues at once. With the
help of multilevel gate units, the valuable context information from the
encoder can be optimally transmitted to the decoder. We design a novel gated
dual branch structure to build the cooperation among different levels of
features and improve the discriminability of the whole network. Through the
dual branch design, more details of the saliency map can be further restored.
In addition, we adopt the atrous spatial pyramid pooling based on the proposed
"Fold" operation (Fold-ASPP) to accurately localize salient objects of various
scales. Extensive experiments on five challenging datasets demonstrate that the
proposed model performs favorably against most state-of-the-art methods under
different evaluation metrics.

该研究提出了一种基于 GateNet 和 Fold-ASPP 的神经网络结构，能够通过多层门单元实现更好的编解码器信息交流控制，并在五个数据集上表现优异，用于显著目标检测。