从Scanpath学习视觉行为
Learning Oculomotor Behaviors from Scanpath
摘要：
识别与眼动跟踪应用相关的眼动行为是一项关键但通常具有挑战性的任务。为了从现有的眼动跟踪数据中自动学习和提取知识，我们开发了一种新的方法，该方法创建了丰富的眼动扫描路径表示，以促进下游任务的学习。所提出的刺激不可知的眼动行为框架（OBF）模型从无监督和半监督任务（包括重建、预测编码、注视识别和对比学习任务）中学习人类的眼动行为。得到的预训练OBF模型可以用于各种应用。我们的预训练模型在自闭症谱系障碍和视觉刺激分类任务中优于基线方法和传统的扫描路径方法。消融实验进一步表明，我们提出的方法可以实现更好的结果与更大的模型尺寸和更多样化的眼动跟踪训练数据集，支持模型的潜力，为未来的眼动跟踪应用。开放源代码：http://github.com/BeibinLi/OBF。
英文摘要：
Identifying oculomotor behaviors relevant for eye-tracking applications is a critical but often challenging task. Aiming to automatically learn and extract knowledge from existing eye-tracking data, we develop a novel method that creates rich representations of oculomotor scanpaths to facilitate the learning of downstream tasks. The proposed stimulus-agnostic Oculomotor Behavior Framework (OBF) model learns human oculomotor behaviors from unsupervised and semi-supervised tasks, including reconstruction, predictive coding, fixation identification, and contrastive learning tasks. The resultant pre-trained OBF model can be used in a variety of applications. Our pre-trained model outperforms baseline approaches and traditional scanpath methods in autism spectrum disorder and viewed-stimulus classification tasks. Ablation experiments further show our proposed method could achieve even better results with larger model sizes and more diverse eye-tracking training datasets, supporting the model's potential for future eye-tracking applications. Open source code: http://github.com/BeibinLi/OBF.
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。
