FEW-SHOT GAZE ESTIMATION WITH MODEL OFFSET PREDICTORS

Due to the variance of optical properties across different people, the performance of a person-agnostic gaze estimation model may not generalize well on a specific person. Though one may achieve better performance by training a person-specific model, it typically requires a large number of samples which is not available in real-life scenarios. Hence, few-shot gaze estimation method is preferred for the small number of samples from a target person. However, the key question is how to close the performance gap between a ''few-shot'' model and the ''many-shot'' model. In this paper, we propose to learn a person-specific offset predictor which outputs the difference between the person-agnostic model and the many-shot person-specific model with as few as one training sample. We adapt the knowledge to a new person by using the average of meta-learned offset predictors parameters as the initialization of the new offset predictor. Experiments show that the proposed few-shot person-specific model is not only closer to the corresponding many-shot person-specific model but also has better accuracy than the SOTA few-shot gaze estimation methods in multiple gaze datasets.