Denoising Distillation Makes Event-Frame Transformers as Accurate Gaze
  Trackers
Jiading Li Zhiyu Zhu Jinhui Hou Junhui Hou Jinjian Wu
This paper tackles the problem of passive gaze estimation using both event
and frame data. Considering inherently different physiological structures, it's
intractable to accurately estimate purely based on a given state. Thus, we
reformulate the gaze estimation as the quantification of state transitions from
the current state to several prior registered anchor states. Technically, we
propose a two-stage learning-based gaze estimation framework to divide the
whole gaze estimation process into a coarse-to-fine process of anchor state
selection and final gaze location. Moreover, to improve generalization ability,
we align a group of local experts with a student network, where a novel
denoising distillation algorithm is introduced to utilize denoising diffusion
technique to iteratively remove inherent noise of event data. Extensive
experiments demonstrate the effectiveness of the proposed method, which greatly
surpasses state-of-the-art methods by a large extent of 15$\%$. The code will
be publicly available at
https://github.com/jdjdli/Denoise_distill_EF_gazetracker.