Predicting Visual Importance Across Graphic Design Types
This work addresses the challenge of poor generalization in visual importance prediction for designers and researchers, though it is incremental as it builds on existing saliency and importance methods by unifying them.
The paper tackles the problem of predicting visual importance across diverse graphic design types and natural images by introducing a unified model (UMSI) that eliminates the need for manual input classification, achieving effective generalization without user labeling. It also presents a new dataset (Imp1k) and demonstrates applications in design tools for adjusting element importance and reflowing designs.
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 specialized datasets, making them limited in application and leading to poor generalization on novel image classes, while requiring a user to know which model to apply to which input. UMSI is a deep learning-based model simultaneously 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 label 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. The model, code, and importance dataset are available at https://predimportance.mit.edu .