Gaze Relevant Feature

Gaze-relevant features are crucial for understanding human behavior and intention, driving research across diverse fields like autism diagnosis, human-computer interaction, and autonomous driving. Current research focuses on developing robust and generalizable methods for extracting these features from various data sources (e.g., video, images, audio), employing techniques like deep learning models (including transformers and convolutional neural networks), and incorporating physics-based constraints to improve accuracy and cross-domain performance. This work has significant implications for improving diagnostic tools, enhancing human-robot interaction, and enabling safer and more efficient autonomous systems.