论文标题
扫描预测的概率时间发展方法
A Probabilistic Time-Evolving Approach to Scanpath Prediction
论文作者
论文摘要
人类视觉关注是一种复杂的现象，已经研究了数十年。其中，扫描路径预测的特定问题构成了挑战，尤其是由于观察者间和观察者内变异性以及其他原因。此外，大多数现有的扫描预测方法都重点是优化鉴于先前的凝视点的预测。在这项工作中，我们基于贝叶斯深度学习提出了一种概率的时间不断发展的扫描预测方法。我们使用新型时空损耗函数优化了模型，基于Kullback-Leibler Divergence和动态时间扭曲的组合，共同考虑了扫描路径的空间和时间维度。我们的扫描预测框架产生的结果表现优于当前最新方法的范围，并且几乎与人类基线相提并论，这表明我们的模型能够产生扫描路径，其行为与真实的行为非常相似。
Human visual attention is a complex phenomenon that has been studied for decades. Within it, the particular problem of scanpath prediction poses a challenge, particularly due to the inter- and intra-observer variability, among other reasons. Besides, most existing approaches to scanpath prediction have focused on optimizing the prediction of a gaze point given the previous ones. In this work, we present a probabilistic time-evolving approach to scanpath prediction, based on Bayesian deep learning. We optimize our model using a novel spatio-temporal loss function based on a combination of Kullback-Leibler divergence and dynamic time warping, jointly considering the spatial and temporal dimensions of scanpaths. Our scanpath prediction framework yields results that outperform those of current state-of-the-art approaches, and are almost on par with the human baseline, suggesting that our model is able to generate scanpaths whose behavior closely resembles those of the real ones.
