Cultural heritage understanding and preservation is an important issue for society as it represents a fundamental aspect of its identity. Paintings represent a significant part of cultural heritage, and are the subject of study continuously. However, the way viewers perceive paintings is strictly related to the so-called HVS (Human Vision System) behaviour. This paper focuses on the eye-movement analysis of viewers during the visual experience of a certain number of paintings. In further details, we introduce a new approach to predicting human visual attention, which impacts several cognitive functions for humans, including the fundamental understanding of a scene, and then extend it to painting images. The proposed new architecture ingests images and returns scanpaths, a sequence of points featuring a high likelihood of catching viewers' attention. We use an FCNN (Fully Convolutional Neural Network), in which we exploit a differentiable channel-wise selection and Soft-Argmax modules. We also incorporate learnable Gaussian distributions onto the network bottleneck to simulate visual attention process bias in natural scene images. Furthermore, to reduce the effect of shifts between different domains (i.e. natural images, painting), we urge the model to learn unsupervised general features from other domains using a gradient reversal classifier. The results obtained by our model outperform existing state-of-the-art ones in terms of accuracy and efficiency.
翻译:文化遗产的理解和保存是社会的一个重要问题,因为它代表了社会特性的一个基本方面。绘画是文化遗产的一个重要部分,是不断研究的主题。然而,观众对绘画的观感方式与所谓的人类视觉系统(HVS)行为密切相关。本文侧重于在视觉体验某些绘画期间对观众的眼动分析。在进一步细节方面,我们引入了一种新的方法来预测人类视觉关注,这影响到人类的几种认知功能,包括对景象的基本理解,然后扩大到绘画。拟议的新结构图像和返回扫描路径,这是吸引观众高度注意的点的顺序。我们使用FCNN(富革命神经网络),在其中我们利用不同的频道选择和Soft-Argmax模块,我们还将可学习的高山分布纳入网络的瓶颈,以模拟自然景象中的视觉关注过程偏差。此外,我们敦促减少不同区域之间移动模型(i)和扫描路径的顺序,以吸引观众注意的高度注意。我们使用的摄像的注意。我们使用普通的变压的图像,将现有变压的模型,用现有的变压的模型从一般的变压结果去。