  Deep convolutional neural networks have been widely applied in salient object
detection and have achieved remarkable results in this field. However, existing
models suffer from information distortion caused by interpolation during
up-sampling and down-sampling. In response to this drawback, this article
starts from two directions in the network: feature and label. On the one hand,
a novel cascaded interaction network with a guidance module named global-local
aligned attention (GAA) is designed to reduce the negative impact of
interpolation on the feature side. On the other hand, a deep supervision
strategy based on edge erosion is proposed to reduce the negative guidance of
label interpolation on lateral output. Extensive experiments on five popular
datasets demonstrate the superiority of our method.
