Multi-Task Gaze Communication Understanding

Human gaze communication is complex, comprising atomic-level (e.g. mutual, share, etc.) and event-level (e.g. follow, aversion, etc.) behaviours. Various methods have been developed to analyse gaze communication in images, but they typically fall short of fully understanding the complexities of the human gaze in videos. In this paper, we present a multi-task, multimodal model based on Contrastive Language-Image Pre-training (CLIP), designed to jointly predict atomic-level and event-level gaze communication, along with gaze target estimation. Specifically, we leverage the Vision-Language model to capture and utilise the semantic information between the atomic-level and event-level gaze communication categories. Additionally, most datasets in this field lack comprehensive annotations for both levels of gaze communication and detailed gaze target information. Therefore, we present a fully annotated gaze communication dataset, GP-Static++. We validate our model on GP-Static++ and several publicly available datasets, demonstrating its state-of-the-art performance. The dataset and code are available at https://pengc98.github.io/Multi-Task-Gaze-Communication-Understanding/.