Abstract
Visual saliency is an important component of attention. It helps animals survive and can also be used by computer vision applications to filter out irrelevant information from high volumes of data. In this work, we present a new convolutional neural network designed for detecting visual saliency, with architecture and data pre-processing methods specific for the task of visual saliency detection. Experiments carried out with the MIT300 benchmark presented state-of-the-art performance and a parameter reduction of 3/4 compared to similar models.
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