A human Visual System (HVS) has the ability to pay visual attention, which is one of the many functions of the HVS. Despite the many advancements being made in visual saliency prediction, there continues to be room for improvement. Deep learning has recently been used to deal with this task. This study proposes a novel deep learning model based on a Fully Convolutional Network (FCN) architecture. The proposed model is trained in an end-to-end style and designed to predict visual saliency. The entire proposed model is fully training style from scratch to extract distinguishing features. The proposed model is evaluated using several benchmark datasets, such as MIT300, MIT1003, TORONTO, and DUT-OMRON. The quantitative and qualitative experiment analyses demonstrate that the proposed model achieves superior performance for predicting visual saliency.

中文翻译：

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一种用于视觉显着性预测的新型全卷积网络


人类视觉系统（HVS）具有视觉注意力的能力，这是 HVS 的众多功能之一。尽管视觉显着性预测取得了许多进步，但仍有改进的空间。深度学习最近被用来处理这项任务。本研究提出了一种基于全卷积网络（FCN）架构的新型深度学习模型。所提出的模型以端到端的方式进行训练，旨在预测视觉显着性。整个提出的模型是从头开始完全训练风格以提取显着特征。使用多个基准数据集（例如 MIT300、MIT1003、TORONTO 和 DUT-OMRON）评估所提出的模型。定量和定性实验分析表明，所提出的模型在预测视觉显着性方面取得了优异的性能。