2025/06/05 by Ruoyang Hu, Robert A. Jacobs, Hu, Ruoyang +1 · 1 citation
Computer Science · Neuroscience · #Computational Engineering #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face Recognition and Perception #Finance #Neural and Behavioral Psychology Studies #Visual Attention and Saliency Detection #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2506.05487
openalex publication_date 2025/06/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Visual attention is a mechanism closely intertwined with vision and memory. Top-down information influences visual processing through attention. We designed a neural network model inspired by aspects of human visual attention. This model consists of two networks: one serves as a basic processor performing a simple task, while the other processes contextual information and guides the first network through attention to adapt to more complex tasks. After training the model and visualizing the learned attention response, we discovered that the model's emergent attention patterns corresponded to spatial and feature-based attention. This similarity between human visual attention and attention in computer vision suggests a promising direction for studying human cognition using neural network models.