Artificial vision has often been described as one of the key remaining challenges to be
solved before machines can act intelligently. Recent developments in a branch of machine …

It has become accepted in the neuroscience community that perception and performance
are quintessentially multisensory by nature. Using the full palette of modern brain imaging …

In this paper, we present a novel end-to-end learning neural network, ie, MATNet, for zero-
shot video object segmentation (ZVOS). Motivated by the human visual attention behavior …

Modular and distributed coding theories of category selectivity along the human ventral
visual stream have long existed in tension. Here, we present a reconciling framework …

This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired
by the human visual system, we explore whether low-level motion-based grouping cues can …

We propose a strong baseline model for unsupervised feature learning using video data. By
learning to predict missing frames or extrapolate future frames from an input video …

Motion is a strong cue for unsupervised object-level grouping. In this paper, we demonstrate
that motion will be exploited most effectively, if it is regarded over larger time windows …

By 1985 newly devised behavioural and electrophysiological techniques had been used to
track development of infants' acuity, contrast sensitivity and binocularity, and for clinical …

Using a visual-to-auditory sensory-substitution algorithm, congenitally fully blind adults were
taught to read and recognize complex images using" soundscapes"—sounds …

Most recent gains in visual recognition have originated from the inclusion of attention
mechanisms in deep convolutional networks (DCNs). Because these networks are …