Escaping the Low-Frequency Bias: Adversarial Frequency Perturbation for Generalisable Gaze Estimation

Deep learning-based gaze estimation methods suffer severeperformance degradation in cross-domain scenarios. Our frequency anal-ysis reveals that overfitting to source-domain low-frequency componentsassociated with domain-specific factors, such as illumination, background,and skin tone, is a critical cause of cross-domain degradation. Basedon this finding, we propose the Adversarial Low-Frequency Perturba-tion (ALFP) framework, which employs a Perturbation Generation Net-work (PGNet) to adversarially generate instance-specific perturbationimages and mix their low-frequency amplitude statistics with those ofsource images through an AdaIN-based mechanism. The resulting inputs,with perturbed low-frequency distributions, are used to train the GazeEstimation Network (GazeNet). Adversarial training between PGNet andGazeNet progressively mitigates GazeNet’s overfitting to source-domainlow-frequency components, thereby improving cross-domain generaliza-tion. Experiments show that ALFP outperforms state-of-the-art meth-ods across various cross-domain settings, improving over the baseline byup to 34.9% and the best existing method by 15.0%, without requiringtarget-domain data.