This research introduces a novel approach that leverages language models with visual data to dramatically improve gaze estimation accuracy.

• Integrates CLIP model capabilities to enhance traditional vision-only gaze tracking
• Demonstrates how textual information complements visual signals for more precise gaze detection
• Creates a multimodal framework that outperforms conventional methods
• Offers significant implications for security applications including user authentication and attention monitoring

For security professionals, this advancement means more reliable biometric systems, enhanced threat detection through gaze analysis, and improved surveillance capabilities with greater precision in tracking subject attention.

GazeCLIP: Enhancing Gaze Estimation Through Text-Guided Multimodal Learning