用无监督域适应生成画作眼动轨迹，更真实还原观画者视线路径。
SPGen: Stochastic scanpath generation for paintings using unsupervised domain adaptation
- 基于可微注视选择与可学习高斯先验模拟自然视觉偏好。
- 通过梯度反转层实现从自然图像到艺术作品的跨域知识迁移。
- 引入随机噪声采样，捕捉眼动数据固有的随机性，适合艺术分析研究。
理解人类视觉注意力对文化遗产保护至关重要。我们提出SPGen，一种新型深度学习模型，用于预测观众观看画作时的扫描路径（即眼动序列）。该模型采用全卷积神经网络（FCNN），结合可微注视选择机制和可学习的高斯先验，模拟自然观看行为中的偏倚。为解决自然图像与艺术品之间的领域差异，采用无监督域适应方法，通过梯度反转层实现从自然场景到画作的知识迁移。此外，引入随机噪声采样器以建模眼动数据固有的随机性。大量实验表明，SPGen优于现有方法，为分析凝视行为、推动艺术珍品的保护与欣赏提供了有力工具。
原文摘要 · Abstract (English)
Understanding human visual attention is key to preserving cultural heritage We introduce SPGen a novel deep learning model to predict scanpaths the sequence of eye movementswhen viewers observe paintings. Our architecture uses a Fully Convolutional Neural Network FCNN with differentiable fixation selection and learnable Gaussian priors to simulate natural viewing biases To address the domain gap between photographs and artworks we employ unsupervised domain adaptation via a gradient reversal layer allowing the model to transfer knowledge from natural scenes to paintings Furthermore a random noise sampler models the inherent stochasticity of eyetracking data. Extensive testing shows SPGen outperforms existing methods offering a powerful tool to analyze gaze behavior and advance the preservation and appreciation of artistic treasures.
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