Predicting Visual Importance Across Graphic Design Types

no code implementations • 7 Aug 2020 • Camilo Fosco, Vincent Casser, Amish Kumar Bedi, Peter O'Donovan, Aaron Hertzmann, Zoya Bylinskii

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.

Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0

no code implementations • 24 Jan 2018 • Colm V. Gallagher, Kevin Leahy, Peter O'Donovan, Ken Bruton, Dominic T. J. O'Sullivan

20 baseline energy models are developed using an exhaustive approach with the optimal model being used to quantify savings.

BIG-bench Machine Learning feature selection +1

Learning Visual Importance for Graphic Designs and Data Visualizations

1 code implementation • 8 Aug 2017 • Zoya Bylinskii, Nam Wook Kim, Peter O'Donovan, Sami Alsheikh, Spandan Madan, Hanspeter Pfister, Fredo Durand, Bryan Russell, Aaron Hertzmann

Our models are neural networks trained on human clicks and importance annotations on hundreds of designs.

Retrieval

Exploratory Font Selection Using Crowdsourced Attributes

no code implementations • ACM Transactions on Graphics 2014 • Peter O'Donovan, Jānis Lībeks, Aseem Agarwala, Aaron Hertzmann

These tools are complementary; a user may search for "graceful" fonts, select a reasonable one, and then refine the results from a list of fonts similar to the selection.

Attribute Descriptive