论文标题
深度神经网络中的隐性显着性
Implicit Saliency in Deep Neural Networks
论文作者
论文摘要
在本文中，我们表明，现有的识别和本地化尚未暴露于眼睛跟踪数据或任何显着性数据集的深度体系结构能够预测人类的视觉显着性。我们将其称为深度神经网络中的隐含显着性。我们以无监督的方式使用预期不匹配的假设来计算这种隐含的显着性。我们的实验表明，以这种方式提取显着性时，使用与最先进的监督算法进行测量时，可以提供可比的性能。此外，当我们在输入图像中添加大噪声时，鲁棒性优于这些算法。另外，我们表明语义特征贡献了人类视觉显着性检测的低级特征。
In this paper, we show that existing recognition and localization deep architectures, that have not been exposed to eye tracking data or any saliency datasets, are capable of predicting the human visual saliency. We term this as implicit saliency in deep neural networks. We calculate this implicit saliency using expectancy-mismatch hypothesis in an unsupervised fashion. Our experiments show that extracting saliency in this fashion provides comparable performance when measured against the state-of-art supervised algorithms. Additionally, the robustness outperforms those algorithms when we add large noise to the input images. Also, we show that semantic features contribute more than low-level features for human visual saliency detection.
