{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/agent-guided-gaze-estimation-network-by-two","title":"Agent-Guided Gaze Estimation Network by Two-Eye Asymmetry Exploration","arxiv_id":null,"date":"2024-10-30","proceeding":"IEEE International Conference on Image Processing (ICIP) 2024 10","authors":["Yichen Shi","Feifei Zhang","Wenming Yang","Guijin Wang","Nan Su"],"abstract":"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.","url_abs":"https://ieeexplore.ieee.org/abstract/document/10648029","url_pdf":"https://ieeexplore.ieee.org/abstract/document/10648029","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"agent-guided-gaze-estimation-network-by-two","repo_url":"https://github.com/iszff/AGE-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"gaze-estimation","task_name":"Gaze Estimation"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/gaze-estimation-on-eyediap","task":"Gaze Estimation","dataset":"EYEDIAP","model":"AGE-Net","rank_in_archive_order":1,"of":1,"metrics":{"Mean Angle Error":"4.78"},"uses_additional_data":false},{"leaderboard":"/sota/gaze-estimation-on-mpii-gaze","task":"Gaze Estimation","dataset":"MPII Gaze","model":"AGE-Net","rank_in_archive_order":2,"of":6,"metrics":{"Angular Error":"3.64"},"uses_additional_data":false},{"leaderboard":"/sota/gaze-estimation-on-mpiigaze-1","task":"Gaze Estimation","dataset":"MPIIGaze","model":"AGE-Net","rank_in_archive_order":1,"of":1,"metrics":{"Mean Angle Error":"3.61"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}