Understanding where people look in images is an important problem in computer vision.
Despite significant research, it remains unclear to what extent human fixations can be predicted …

Here we present DeepGaze II, a model that predicts where people look in images. The
model uses the features from the VGG-19 deep neural network trained to identify objects in …

Drivers’ hazard perception ability, as measured in video-based simulations, correlates with
crash involvement, improves with experience and can be trained. We propose two alternative …

Learning the properties of an image associated with human gaze placement is important
both for understanding how biological systems explore the environment and for computer …

Dozens of new models on fixation prediction are published every year and compared on open
benchmarks such as MIT300 and LSUN. However, progress in the field can be difficult to …

With the rise of machines to human-level performance in complex recognition tasks, a
growing amount of work is directed toward comparing information processing in humans and …

Humans typically move their eyes in “scanpaths” of fixations linked by saccades. Here we
present DeepGaze III, a new model that predicts the spatial location of consecutive fixations in …

Our visual environment is full of texture—“stuff” like cloth, bark, or gravel as distinct from “things”
like dresses, trees, or paths—and humans are adept at perceiving subtle variations in …

Feature visualizations such as synthetic maximally activating images are a widely used
explanation method to better understand the information processing of convolutional neural …

10.7554/eLife.42512.001 We subjectively perceive our visual field with high fidelity, yet
peripheral distortions can go unnoticed and peripheral objects can be difficult to identify (…