Institute of New Imaging Technologies, Universitat Jaume I, Av. Vicent Sos Baynat, s/n, E12071 Castellón, Spain.
Universitat Jaume I, Av. Vicent Sos Baynat, s/n, E12071 Castellón, Spain.
Sensors (Basel). 2021 Apr 29;21(9):3099. doi: 10.3390/s21093099.
Temporal salience considers how visual attention varies over time. Although visual salience has been widely studied from a spatial perspective, its temporal dimension has been mostly ignored, despite arguably being of utmost importance to understand the temporal evolution of attention on dynamic contents. To address this gap, we proposed Glimpse, a novel measure to compute temporal salience based on the observer-spatio-temporal consistency of raw gaze data. The measure is conceptually simple, training free, and provides a semantically meaningful quantification of visual attention over time. As an extension, we explored scoring algorithms to estimate temporal salience from spatial salience maps predicted with existing computational models. However, these approaches generally fall short when compared with our proposed gaze-based measure. Glimpse could serve as the basis for several downstream tasks such as segmentation or summarization of videos. Glimpse's software and data are publicly available.
时间显著性考虑视觉注意力随时间的变化。尽管从空间角度广泛研究了视觉显著性,但它的时间维度在很大程度上被忽略了,尽管可以说这对于理解动态内容上注意力的时间演变至关重要。为了解决这一差距,我们提出了 Glimpse,这是一种基于原始注视数据的观察者时空一致性来计算时间显著性的新方法。该方法概念简单,无需训练,并且可以对随时间变化的视觉注意力进行语义上有意义的量化。作为扩展,我们探索了评分算法,以便从使用现有计算模型预测的空间显著性图中估计时间显著性。然而,与我们提出的基于注视的方法相比,这些方法通常存在不足。Glimpse 可以作为视频分割或摘要等下游任务的基础。Glimpse 的软件和数据都是公开的。