Human Visual Saliency

Human visual saliency research focuses on understanding how humans prioritize visual information, aiming to replicate this ability in computer vision systems. Current research emphasizes integrating human-perceived saliency into model training, often using attention mechanisms and incorporating saliency maps (derived from eye-tracking or annotations) into loss functions to improve model generalization and performance, particularly for challenging tasks like object recognition and biometric security. This work is significant because it bridges the gap between human perception and artificial intelligence, leading to more robust and accurate computer vision systems with applications in diverse fields such as robotics and security.