{"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/fixatons-a-collection-of-human-fixations","title":"FixaTons: A collection of Human Fixations Datasets and Metrics for Scanpath Similarity","arxiv_id":"1802.02534","date":"2018-02-07","proceeding":null,"authors":["Dario Zanca","Valeria Serchi","Pietro Piu","Francesca Rosini","Alessandra Rufa"],"abstract":"In the last three decades, human visual attention has been a topic of great\ninterest in various disciplines. In computer vision, many models have been\nproposed to predict the distribution of human fixations on a visual stimulus.\nRecently, thanks to the creation of large collections of data, machine learning\nalgorithms have obtained state-of-the-art performance on the task of saliency\nmap estimation. On the other hand, computational models of scanpath are much\nless studied. Works are often only descriptive or task specific. This is due to\nthe fact that the scanpath is harder to model because it must include the\ndescription of a dynamic. General purpose computational models are present in\nthe literature, but are then evaluated in tasks of saliency prediction, losing\ntherefore information about the dynamics and the behaviour. In addition, two\ntechnical reasons have limited the research. The first reason is the lack of\nrobust and uniformly used set of metrics to compare the similarity between\nscanpath. The second reason is the lack of sufficiently large and varied\nscanpath datasets. In this report we want to help in both directions. We\npresent FixaTons, a large collection of datasets human scanpaths (temporally\nordered sequences of fixations) and saliency maps. It comes along with a\nsoftware library for easy data usage, statistics calculation and implementation\nof metrics for scanpath and saliency prediction evaluation.","url_abs":"http://arxiv.org/abs/1802.02534v3","url_pdf":"http://arxiv.org/pdf/1802.02534v3.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":"fixatons-a-collection-of-human-fixations","repo_url":"https://github.com/dariozanca/FixaTons","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"descriptive","task_name":"Descriptive"},{"task_slug":"saliency-prediction","task_name":"Saliency Prediction"}],"methods":[],"datasets_introduced":[{"slug":"fixatons","name":"FixaTons","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}