Neuroscientists have long characterized the properties and functions of the nervous system,
and are increasingly succeeding in answering how brains perform the tasks they do. But the …

Artificial intelligence powered by deep neural networks has reached a level of complexity
where it can be difficult or impossible to express how a model makes its decisions. This …

Predicting salient regions in natural images requires the detection of objects that are present
in a scene. To develop robust representations for this challenging task, high-level visual …

Inattentional blindness is the psychological phenomenon that causes one to miss things in
plain sight. It is a consequence of the selective attention in perception that lets us remain …

We present a new end-to-end network architecture for facial expression recognition with an
attention model. It focuses attention in the human face and uses a Gaussian space …

For more than 100 years we have known that the visual field is mapped onto the surface of
visual cortex, imposing an inherently spatial reference frame on visual information …

Highlights•Deep neural networks (DNNs) now reach human-level performance on some
perceptual tasks.•They show human-like error patterns and predict sensory cortical …

Mammals localize sounds using information from their two ears. Localization in real-world
conditions is challenging, as echoes provide erroneous information and noises mask parts …

A class-agnostic tracker typically consists of three key components, ie, its motion model, its
target appearance model, and its updating strategy. However, most recent top-performing …

The goal of this work is to characterize the representational impact that foveation operations
have for machine vision systems, inspired by the foveated human visual system, which has …