Predicting Visual Importance Across Graphic Design Types
C. Fosco, V. Casser, A. K. Bedi, P. O’Donovan, A. Hertzmann and Z. Bylinskii: “Predicting Visual Importance Across Graphic Design Types.” ACM User Interface Software and Technology Symposium (UIST’20). (full text)
This paper introduces a Unified Model of Saliency and Importance (UMSI), which learns to predict visual importance in input graphic designs, and saliency in natural images, along with a new dataset and applications. Previous methods for predicting saliency or visual importance are trained individually on small datasets, making them very limited in application and leading to poor generalization on novel designs, while requiring a user to know which model to apply to which input. UMSI is trained on images from different design classes, including posters, infographics, mobile UIs, as well as natural images, and includes an automatic classification module to classify the input. This allows the model to work more effectively without requiring a user to classify the input. We also introduce Imp1k, a new dataset of designs annotated with importance information. We demonstrate two new design interfaces that use importance prediction, including a tool for adjusting the relative importance of design elements, and a tool for reflowing designs to new aspect ratios while preserving visual importance.