  Deep neural networks have significantly improved appearance-based gaze
estimation accuracy. However, it still suffers from unsatisfactory performance
when generalizing the trained model to new domains, e.g., unseen environments
or persons. In this paper, we propose a plug-and-play gaze adaptation framework
(PnP-GA), which is an ensemble of networks that learn collaboratively with the
guidance of outliers. Since our proposed framework does not require
ground-truth labels in the target domain, the existing gaze estimation networks
can be directly plugged into PnP-GA and generalize the algorithms to new
domains. We test PnP-GA on four gaze domain adaptation tasks, ETH-to-MPII,
ETH-to-EyeDiap, Gaze360-to-MPII, and Gaze360-to-EyeDiap. The experimental
results demonstrate that the PnP-GA framework achieves considerable performance
improvements of 36.9%, 31.6%, 19.4%, and 11.8% over the baseline system. The
proposed framework also outperforms the state-of-the-art domain adaptation
approaches on gaze domain adaptation tasks.
