Decoding Attention from Gaze: A Benchmark Dataset and End-to-End Models
This work addresses the problem of automating attention analysis in natural scenes for researchers in cognitive science and computer vision, though it is incremental as it builds on existing computer vision tools.
The paper tackled the challenge of analyzing eye-tracking data for attention decoding in complex visual stimuli by introducing the MOET dataset and proposing two end-to-end deep learning models, which outperformed state-of-the-art heuristic methods.
Eye-tracking has potential to provide rich behavioral data about human cognition in ecologically valid environments. However, analyzing this rich data is often challenging. Most automated analyses are specific to simplistic artificial visual stimuli with well-separated, static regions of interest, while most analyses in the context of complex visual stimuli, such as most natural scenes, rely on laborious and time-consuming manual annotation. This paper studies using computer vision tools for "attention decoding", the task of assessing the locus of a participant's overt visual attention over time. We provide a publicly available Multiple Object Eye-Tracking (MOET) dataset, consisting of gaze data from participants tracking specific objects, annotated with labels and bounding boxes, in crowded real-world videos, for training and evaluating attention decoding algorithms. We also propose two end-to-end deep learning models for attention decoding and compare these to state-of-the-art heuristic methods.