Systems and methods of learning visual importance for graphic design and data visualization

Abstract

Embodiments disclosed herein describe systems, methods, and products that train one or more neural networks and execute the trained neural network across various applications. The one or more neural networks are trained to optimize a loss function comprising a pixel-level comparison between the outputs generated by the neural networks and the ground truth dataset generated from a bubble view methodology or an explicit importance maps methodology. Each of these methodologies may be more efficient than and may closely approximate the more expensive but accurate human eye gaze measurements. The embodiments herein leverage an existing process for training neural networks to generate importance maps of a plurality of graphic objects to offer interactive applications for graphics designs and data visualizations. Based on the importance maps, the computer may provide real-time design feedback, generate smart thumbnails of the graphic objects, provide recommendations for design retargeting, and extract smart color themes from the graphic objects.