A Transformer-Based Model for the Prediction of Human Gaze Behavior on Videos

arXiv:2404.07351v1 Announce Type: cross
Abstract: Eye-tracking applications that utilize the human gaze in video understanding tasks have become increasingly important. To effectively automate the process of video analysis based on eye-tracking data, it is important to accurately replicate human gaze behavior. However, this task presents significant challenges due to the inherent complexity and ambiguity of human gaze patterns. In this work, we introduce a novel method for simulating human gaze behavior. Our approach uses a transformer-based reinforcement learning algorithm to …

abstract analysis applications arxiv automate become behavior challenges cs.cv cs.hc cs.lg data however human prediction process replicate tasks tracking tracking data transformer type understanding video video analysis videos video understanding