One of the most fundamental and information-laden actions humans do is to look at objects. However, a survey of current works reveals that existing gaze-related datasets annotate only the pixel being looked at, and not the boundaries of a specific object of interest. This lack of object annotation presents an opportunity for further advancing gaze estimation research. To this end, we present a challenging new task called gaze object prediction, where the goal is to predict a bounding box for a person's gazed-at object. To train and evaluate gaze networks on this task, we present the Gaze On Objects (GOO) dataset. GOO is composed of a large set of synthetic images (GOO Synth) supplemented by a smaller subset of real images (GOO-Real) of people looking at objects in a retail environment. Our work establishes extensive baselines on GOO by re-implementing and evaluating selected state-of-the art models on the task of gaze following and domain adaptation. Code is available on github.

介绍了一项名为"注视物体预测"的新任务，旨在预测人类注视的物体的边界框。为了在此任务上训练和评估注视网络，提出了一个名为Gaze On Objects (GOO)数据集，由一个大型的合成图像数据集(GOO Synth)和少量的真实图像数据集(GOO-Real)组成，在零标注物体的情况下，通过对目标检测技术的引入，实现了注视目标物体的预测和估计。

GOO：零售环境中注视对象预测数据集