TurkEyes: A Web-Based Toolbox for Crowdsourcing Attention Data
Anelise Newman, Barry A. McNamara, Camilo Fosco, Yun Bin Zhang, Pat Sukhum, Matthew Tancik, Nam Wook Kim, Zoya Bylinskii
摘要
Eye movements provide insight into what parts of an image a viewer finds most salient, interesting, or relevant to the task at hand. Unfortunately, eye tracking data, a commonly-used proxy for attention, is cumbersome to collect. Here we explore an alternative: a comprehensive web-based toolbox for crowdsourcing visual attention. We draw from four main classes of attention-capturing methodologies in the literature. ZoomMaps is a novel zoom-based interface that captures viewing on a mobile phone. CodeCharts is a self-reporting methodology that records points of interest at precise viewing durations. ImportAnnots is an "annotation" tool for selecting important image regions, and cursor-based BubbleView lets viewers click to deblur a small area. We compare these methodologies using a common analysis framework in order to develop appropriate use cases for each interface. This toolbox and our analyses provide a blueprint for how to gather attention data at scale without an eye tracker.
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引用它的顶会 Paper4
- Predicting Visual Importance Across Graphic Design TypesCamilo Fosco, Vincent Casser, Amish Kumar Bedi, Peter O'Donovan 等UIST 2020 · 被引用 55 次
- The State of Pilot Study Reporting in Crowdsourcing: A Reflection on Best Practices and GuidelinesJonas Oppenlaender, Tahir Abbas, Ujwal GadirajuCSCW 2024 · 被引用 10 次
- Tell Me Without Telling Me: Two-Way Prediction of Visualization Literacy and Visual AttentionMinsuk Chang, Yao Wang, Huichen Will Wang, Yuanhong Zhou 等IEEE VIS 2025 · 被引用 1 次
- How Much Time Do You Have? Modeling Multi-Duration SaliencyCamilo Fosco, Anelise Newman, Pat Sukhum, Yun Bin Zhang 等CVPR 2020
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