Jun 3, 2020 – 11:00 AM
Fast reactions to changes in the surrounding visual environment require efficient attention mechanisms to reallocate computational resources to the most relevant locations in the visual field. While current computational models keep improving their predictive ability thanks to the increasing availability of data, they still struggle to approximate the effectiveness and efficiency exhibited by foveated animals.
In this paper, we present a biologically-plausible computational model of focus of attention that exhibits spatiotemporal locality and is very well-suited for parallel
and distributed implementations. Attention emerges as a wave propagation process originated by visual stimuli corresponding to details and motion information. The resulting field obeys the principle of “inhibition of return” so as not to get stuck in potential holes.
An accurate experimentation of the model shows that it achieves top-level performance in scanpath prediction tasks. This can easily be understood when considering that, as the velocity of wave propagation goes to infinity, the proposed model reduces to the recently proposed state of the art gravitational models of focus of attention.