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Deep convolutional neural networks have been widely applied in salient objectdetection and have achieved remarkable results in this field. However, existingmodels suffer from information distortion caused by interpolation duringup-sampling and down-sampling. In response to this drawback, this articlestarts from two directions in the network: feature and label. On the one hand,a novel cascaded interaction network with a guidance module named global-localaligned attention (GAA) is designed to reduce the negative impact ofinterpolation on the feature side. On the other hand, a deep supervisionstrategy based on edge erosion is proposed to reduce the negative guidance oflabel interpolation on lateral output. Extensive experiments on five populardatasets demonstrate the superiority of our method.

Cascaded Interaction with Eroded Deep Supervision for Salient Object Detection