{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-visual-importance-for-graphic","title":"Learning Visual Importance for Graphic Designs and Data Visualizations","arxiv_id":"1708.02660","date":"2017-08-08","proceeding":null,"authors":["Zoya Bylinskii","Nam Wook Kim","Peter O'Donovan","Sami Alsheikh","Spandan Madan","Hanspeter Pfister","Fredo Durand","Bryan Russell","Aaron Hertzmann"],"abstract":"Knowing where people look and click on visual designs can provide clues about\nhow the designs are perceived, and where the most important or relevant content\nlies. The most important content of a visual design can be used for effective\nsummarization or to facilitate retrieval from a database. We present automated\nmodels that predict the relative importance of different elements in data\nvisualizations and graphic designs. Our models are neural networks trained on\nhuman clicks and importance annotations on hundreds of designs. We collected a\nnew dataset of crowdsourced importance, and analyzed the predictions of our\nmodels with respect to ground truth importance and human eye movements. We\ndemonstrate how such predictions of importance can be used for automatic design\nretargeting and thumbnailing. User studies with hundreds of MTurk participants\nvalidate that, with limited post-processing, our importance-driven applications\nare on par with, or outperform, current state-of-the-art methods, including\nnatural image saliency. We also provide a demonstration of how our importance\npredictions can be built into interactive design tools to offer immediate\nfeedback during the design process.","url_abs":"http://arxiv.org/abs/1708.02660v1","url_pdf":"http://arxiv.org/pdf/1708.02660v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-visual-importance-for-graphic","repo_url":"https://github.com/egorabaturov/visimportance-in-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.02660","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}