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The proposed method delivers highly competitive performance and shows high consistency with the human visual system (HVS) on the public SCI-oriented databases.A variety of computer vision tasks benefit significantly from increasingly powerful deep convolutional neural networks. However, the inherently local property of convolution operations prevents most existing models from capturing long-range feature interactions for improved performances. In this paper, we propose a novel module, called Spatially-Aware Context (SAC) block, to learn
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