  This paper presents a method for gaze estimation according to face images. We
train several gaze estimators adopting four different network architectures,
including an architecture designed for gaze estimation (i.e.,iTracker-MHSA) and
three originally designed for general computer vision tasks(i.e., BoTNet,
HRNet, ResNeSt). Then, we select the best six estimators and ensemble their
predictions through a linear combination. The method ranks the first on the
leader-board of ETH-XGaze Competition, achieving an average angular error of
$3.11^{\circ}$ on the ETH-XGaze test set.
