Agent-Guided Gaze Estimation Network by Two-Eye Asymmetry Exploration

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.