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.

What jumps out in a single glance of an image is different than what you might notice after closer inspection. Yet conventional models of visual saliency produce predictions at an arbitrary, fixed viewing duration, offering a limited view of the rich interactions between image content and gaze location. In this paper we propose to capture gaze as a series of snapshots, by generating population-level saliency heatmaps for multiple viewing durations. We collect the CodeCharts1K dataset, which contains multiple distinct heatmaps per image corresponding to 0.5, 3, and 5 seconds of free-viewing. We develop an LSTM-based model of saliency that simultaneously trains on data from multiple viewing durations. Our Multi-Duration Saliency Excited Model (MD-SEM) achieves competitive performance on the LSUN 2017 Challenge with 57% fewer parameters than comparable architectures. It is the first model that produces heatmaps at multiple viewing durations, enabling applications where multi-duration saliency can be used to prioritize visual content to keep, transmit, and render.