Semi-Supervised Gaze Estimation via Disentangled Subspace Contrastive Learning
Appearance-based gaze estimation always suffers from poor generalization due to limited annotated samples and insufficient dataset diversity. Leading approaches adopt weakly supervised learning to generate large-scale pseudo-labeled data from unconstrained real-world scenarios, aiming to mitigate the domain shifts. In this work, we devise a simple yet effective semi-supervised learning architecture that leverages unlabeled data to enhance domain generalization, thereby reducing reliance on labor-intensive manual annotations. Our key insight is to impose Jacobian regularization to disentangle feature representations into discriminative subspaces dedicated to specific gaze components, such ...
| Authors | Qida Tan, Hongyu Yang, Wenchao Du |
|---|---|
| Primary category | cs.CV |
| Published | 26 May 2026 |
| Last updated at source | 26 May 2026 |
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