论文标题
观察者间一致的视觉扫描预测的深层对抗训练
An Inter-observer consistent deep adversarial training for visual scanpath prediction
论文作者
论文摘要
视觉扫描Path是一系列点的序列，在探索场景的同时，人类凝视通过这些点。它代表了视觉注意力研究所基于的基本概念。结果，近年来，预测它们的能力已成为重要的任务。在本文中，我们提出了一种通过轻量级深度神经网络进行扫描预测的观察者间一致的对抗训练方法。对抗方法采用歧视性神经网络作为动态损失，更适合对自然随机现象进行建模，同时保持与不同观察者穿越扫描路径的主观性质之间的分布之间的一致性。通过广泛的测试，我们展示了关于最先进方法的方法的竞争力。
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.
