People have the remarkable ability to take in a tremendous amount of information (estimated to be ~1010 bits/s entering the retina) and selectively attend to a few task-relevant and interesting regions for further processing (e.g., memory, comprehension, action). Modeling human attention (the result of which is often called a saliency model) has therefore been of interest across the fields of neuroscience, psychology, human-computer interaction (HCI) and computer vision. The ability to predict which regions are likely to attract attention has numerous important applications in areas like graphics, photography, image compression and processing, and the measurement of visual quality. We’ve previously discussed the possibility of accelerating eye movement research using machine learning and smartphone-based gaze estimation, which earlier required specialized hardware [...]
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