The visual scanpath is a sequence of points through which the human gaze moves while exploring a scene. It represents the fundamental concepts upon which visual attention research is based. As a result, the ability to predict them has emerged as an important task in recent years. In this paper, we propose an inter-observer consistent adversarial training approach for scanpath prediction through a lightweight deep neural network. The adversarial method employs a discriminative neural network as a dynamic loss that is better suited to model the natural stochastic phenomenon while maintaining consistency between the distributions related to the subjective nature of scanpaths traversed by different observers. Through extensive testing, we show the competitiveness of our approach in regard to state-of-the-art methods.

翻译：视觉扫描路径是人类在探索场景时视线移动的一系列点，它代表了视觉注意力研究所基于的基本概念。因此，预测扫描路径的能力已成为近年来的重要任务。本文提出了一种通过轻量级深度神经网络实现观察者间一致的对抗性训练方法，用于扫描路径预测。该对抗方法采用判别神经网络作为动态损失函数，更适用于建模自然的随机现象，同时保持不同观察者遍历扫描路径的主观性相关分布之间的一致性。通过大量测试，我们展示了该方法相对于现有最先进方法的竞争力。