Papers › Agent-Guided Gaze Estimation Network by Two-Eye Asymmetry Exploration
Agent-Guided Gaze Estimation Network by Two-Eye Asymmetry Exploration
Yichen Shi, Feifei Zhang, Wenming Yang, Guijin Wang, Nan Su
Gaze estimation is an important task in understanding human visual attention. Despite the performance gain brought by recent algorithm development, the task remains challenging due to two-eye appearance asymmetry resulting from head pose variation and nonuniform illumination. In this paper, we propose a novel architecture, Agent-guided Gaze Estimation Network (AGE-Net), to make full and efficient use of two-eye features. By exploring the appearance asymmetry and the consequent feature space asymmetry, we devise a main branch and two agent regression tasks. The main branch extracts related features of the left and right eyes from low-level semantics. Meanwhile, the agent regression tasks extract asymmetric features of the left and right eyes from high-level semantics, so as to guide the main branch to learn more about the eye feature space. Experiments show that our method achieves state-of-the-art gaze estimation task performance on both MPIIGaze and EyeDiap datasets.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Gaze Estimation | EYEDIAP | AGE-Net | Mean Angle Error | 4.78 | #1 of 1 | Archive leaderboard | report |
| Gaze Estimation | MPII Gaze | AGE-Net | Angular Error | 3.64 | #2 of 6 | Archive leaderboard | report |
| Gaze Estimation | MPIIGaze | AGE-Net | Mean Angle Error | 3.61 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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