Gaze Perception in Humans and CNN-Based Model

2021/04/17 by Nicole Han, William Yang Wang, Han, Nicole X. +3
Computer Science · Neuroscience · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face Recognition and Perception #Gaze Tracking and Assistive Technology #Visual Attention and Saliency Detection

paper · pdf · doi:10.48550/arxiv.2104.08447

openalex publication_date 2021/04/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Making accurate inferences about other individuals' locus of attention is essential for human social interactions and will be important for AI to effectively interact with humans. In this study, we compare how a CNN (convolutional neural network) based model of gaze and humans infer the locus of attention in images of real-world scenes with a number of individuals looking at a common location. We show that compared to the model, humans' estimates of the locus of attention are more influenced by the context of the scene, such as the presence of the attended target and the number of individuals in the image.

Related

• Understanding More about Human and Machine Attention in Deep Neural Networks
• Visual Attention driven by Convolutional Features
• Connecting Gaze, Scene, and Attention: Generalized Attention Estimation via Joint Modeling of Gaze and Scene Saliency
• When Computer Vision Gazes at Cognition
• Measuring and modeling the perception of natural and unconstrained gaze in humans and machines