LAUSR

Abstract Fully convolutional network (FCN) based semantic segmentation models have largely inspired most recent works in the field of salient object detection. However, the lack of context information summarization can degrade the prediction accuracy of the final saliency map. Moreover, the information loss of down-sampling operations of FCN-based models results in the loss of details of the final saliency map, such as edges of the saliency object. In this paper, we proposed a novel deep convolutional neural network (CNN) by introducing a spatial and channel-wise attention layer into a multi-scale encoder-decoder framework. The attention CNN layer can align the context information between the feature maps at different scales and the final prediction of the saliency map. In addition, a structure with multiple scale side-way outputs was designed to produce more accurate edge-preserving saliency maps by integrating saliency maps at different scales. Experimental results demonstrated the effectiveness of the proposed model on several benchmark datasets. Additional experimental results also validated the potential and feasibility of applying our trained saliency model to other object-driven vision tasks as an efficient preprocessing step.