论文标题
通过心理物理合成图像进行显着预测的噪声扰动
Noise Perturbation for Saliency Prediction with Psychophysical Synthetic Images
论文作者
论文摘要
卷积神经网络（CNN）在自然图像显着性预测方面取得了巨大成功。这项研究的主要目的是研究CNN和经典模型中具有心理物理合成图像的显着性预测的性能。就性能而言，它仍然像自然图像一样体面吗？同时，它可以用于研究CNN与人类视力之间的关系，主要是低级视力功能。另一方面，CNN是人类视觉功能的精确副本吗？这项研究使用了受到低级视觉系统启发的CNN，傅立叶和光谱模型，以研究心理物理合成图像的显着性预测而不是自然图像。根据我们的发现，受傅立叶和光谱理论启发的显着性预测模型优于当前训练的深层神经网络，并在具有噪声扰动的心理物理图像上。但是，与预训练的深度神经网络相比，心理物理模型在噪声中更不稳定。同时，我们建议使用心理物理方法研究CNN可以使视觉神经科学和人工神经网络研究受益。
Convolutional neural networks (CNNs) have achieved great success in natural image saliency prediction. The primary goal of this study is to investigate the performance of saliency prediction in CNN and classic models with psychophysical synthetic images under noise perturbation. Is it still as decent as natural images in terms of performance? In the meantime, it can be used to investigate the relationship between CNNs and human vision, mainly low-level vision functions. On the other hand, are CNNs exact replicas of human visual function? This study used CNNs, Fourier, and spectral models inspired by low-level vision systems to investigate saliency prediction on psychophysical synthetic images rather than natural images. According to our findings, saliency prediction models inspired by Fourier and spectral theory outperformed current pre-trained deep neural networks on psychophysical images with noise perturbation. However, psychophysical models were more unstable in noise than pre-trained deep neural networks. Meanwhile, we suggested that investigating CNNs with psychophysical methods could benefit visual neuroscience and artificial neural network studies.
