PhD student, Zhejiang University
2 papers at NeurIPS 2025
We propose OmniGaze, a semi-supervised learning framework, which utilizes large-scale unlabeled data and reward-driven pseudo-labeling strategy to effectively generalize gaze estimation in the wild.
we propose a framework propagating contextual information with geometric groups and semantic groups.
