Predicting What Drives Human Attention in Photographs, Visualizations, and Graphic Designs
Knowing where a person looks in an image can provide us with important clues about what captures their attention and what may eventually enter their memory. Aggregating the attention patterns of a group of people can help us make conclusions about the effectiveness of a design. Computational models of attention help guide image processing algorithms like automatic image resizing and thumbnailing, they can direct a model to compose more meaningful image captions, and they can be used to provide feedback within graphic design tools. In this talk, I will cover what we know about human attention and how we capture human attention and interest in images at a large data scale using novel crowdsourcing interfaces. I will then demonstrate how we use this data to build computational models of attention for photographs, visualizations, and graphic designs, along with the applications that these models make possible.
Zoya Bylinskii is a Research Scientist in the Creative Intelligence Lab at Adobe Research in Cambridge and an Associate of the Institute of Applied Computational Science at Harvard University. She received a Ph.D. and an M.Sc. in Computer Science from the Massachusetts Institute of Technology in 2018 and 2015, respectively, and an Hon. B.Sc. in Computer Science and Statistics from the University of Toronto in 2012. Zoya is a 2018 Rising Star in EECS, a 2016 Adobe Research Fellow, a 2014 NSERC Postgraduate Scholar, a 2013 Julie Payette Research Scholar, and a 2011 Anita Borg Scholar. Zoya works at the interface of human vision, computer vision, and human-computer interaction.