Fixation Prediction

Fixation prediction focuses on modeling where humans look in visual scenes, aiming to understand visual attention and improve computer vision systems. Current research explores diverse applications, from improving AI training efficiency by leveraging eye-gaze data to enhancing the robustness of object detection and video understanding models through biologically-inspired active vision mechanisms. This field is significant for advancing both fundamental understanding of human visual perception and developing more human-like and effective AI systems across various domains, including medical image analysis, robotics, and virtual reality. Prominent approaches involve deep learning architectures like convolutional and recurrent neural networks, transformers, and Gaussian mixture models, often incorporating techniques like attention mechanisms and multi-scale processing.