Learning to Adapt: Test-Time Personalized Gaze Estimation with Weak and Self-Supervised Learning for In-Vehicle Scenarios

Personalized gaze estimation in in-vehicle settings is crucial for driver monitoring but remains challenging due to individual anatomical differences and environmental variability. Existing approaches often rely on fully supervised fine-tuning, which requires labeled calibration data and limits practical deployment. To overcome this limitation, we propose a Test-Time Personalized Gaze Estimation framework that enables in-vehicle gaze personalization with minimal supervision. Our method builds upon GazeDPTR_V2, which extends gaze estimation to gaze zone classification by leveraging positional features from point projection and visual attributes from images. We introduce a two-stage adaptation strategy: (1) Self-Supervised Test-Time Adaptation, where augmentation-based self-supervision is applied to adapt the model to freely available unlabeled test-time data, facilitated by Model-Agnostic Meta-Learning (MAML) for effective unsupervised adaptation; (2) Weakly-Supervised Personalization, where a small number of gaze zone samples collected from the driver during an initial setup phase are used for fine-tuning. We evaluate our approach on the IVGaze dataset, which provides both gaze direction vectors and gaze zone labels. Experimental results show that our method significantly outperforms baseline models, demonstrating the effectiveness of self-supervised meta-learning and weak supervision for real-world in-vehicle gaze estimation.