Gaze Perception in Humans and CNN-Based Model

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.

ICB Affiliated Authors

Authors
Nicole X. Han, William Yang Wang, Miguel P. Eckstein
Date
Type
Peer-Reviewed Conference Presentation
Journal
International Conference on Learning Representations (ICLR) 2021
City
Vienna
Country
Austria
Emblems