Gaze Prediction
Gaze prediction, the task of estimating where a person is looking, is a rapidly evolving field with applications in human-computer interaction, virtual reality, and driver monitoring. Current research focuses on improving accuracy and efficiency using various deep learning architectures, including transformers, convolutional neural networks, and hybrid models that incorporate multimodal data (e.g., EEG, head pose, scene context). These advancements are driven by the need for robust and reliable gaze estimation in diverse and challenging environments, ultimately impacting fields requiring a deeper understanding of human attention and intention.
Papers
Advancing EEG-Based Gaze Prediction Using Depthwise Separable Convolution and Enhanced Pre-Processing
Matthew L Key, Tural Mehtiyev, Xiaodong Qu
Effect of Kernel Size on CNN-Vision-Transformer-Based Gaze Prediction Using Electroencephalography Data
Chuhui Qiu, Bugao Liang, Matthew L Key
EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architectures
Teng Liang, Andrews Damoah