儿童期至成年期视觉注视预测的计算模型
Computational Model for Predicting Visual Fixations from Childhood to Adulthood
摘要：
人们如何看待视觉信息揭示了关于他们自己、他们的兴趣和他们的精神状态的基本信息。虽然以前的视觉注意力模型输出静态的二维显着性图，但扫视模型的目标不仅是预测观察者看向哪里，而且还预测他们如何移动眼睛来探索场景。在这里，我们表明，扫视模型是一个灵活的框架，可以定制，以模仿观察者的观看倾向。更具体地说，我们使用了来自101名观察者的眼睛数据，这些观察者分为5个年龄组（成年人，8-10岁，6-8你，4-6岁.（2 y.o.）来训练我们的扫视模型，以适应人类视觉系统发展的不同阶段。我们发现，扫视幅度和方向的联合分布是一个特定于每个年龄组的视觉签名，并可用于生成年龄相关的扫描路径。我们的年龄相关的扫视模型不仅输出类似人类的，特定于年龄的视觉扫描路径，但也显着优于其他国家的最先进的显着性模型。在本文中，我们证明了视觉注意的计算建模，通过使用扫视模型，可以有效地适应于模仿一组特定的观察者的凝视行为。
英文摘要：
How people look at visual information reveals fundamental information about themselves, their interests and their state of mind. While previous visual attention models output static 2-dimensional saliency maps, saccadic models aim to predict not only where observers look at but also how they move their eyes to explore the scene. Here we demonstrate that saccadic models are a flexible framework that can be tailored to emulate observer's viewing tendencies. More specifically, we use the eye data from 101 observers split in 5 age groups (adults, 8-10 y.o., 6-8 y.o., 4-6 y.o. and 2 y.o.) to train our saccadic model for different stages of the development of the human visual system. We show that the joint distribution of saccade amplitude and orientation is a visual signature specific to each age group, and can be used to generate age-dependent scanpaths. Our age-dependent saccadic model not only outputs human-like, age-specific visual scanpath, but also significantly outperforms other state-of-the-art saliency models. In this paper, we demonstrate that the computational modelling of visual attention, through the use of saccadic model, can be efficiently adapted to emulate the gaze behavior of a specific group of observers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
