  Despite remarkable advancements, mainstream gaze estimation techniques,
particularly appearance-based methods, often suffer from performance
degradation in uncontrolled environments due to variations in illumination and
individual facial attributes. Existing domain adaptation strategies, limited by
their need for target domain samples, may fall short in real-world
applications. This letter introduces Branch-out Auxiliary Regularization (BAR),
an innovative method designed to boost gaze estimation's generalization
capabilities without requiring direct access to target domain data.
Specifically, BAR integrates two auxiliary consistency regularization branches:
one that uses augmented samples to counteract environmental variations, and
another that aligns gaze directions with positive source domain samples to
encourage the learning of consistent gaze features. These auxiliary pathways
strengthen the core network and are integrated in a smooth, plug-and-play
manner, facilitating easy adaptation to various other models. Comprehensive
experimental evaluations on four cross-dataset tasks demonstrate the
superiority of our approach.
