Behind the Machine's Gaze: Neural Networks with Biologically-inspired Constraints Exhibit Human-like Visual Attention. (arXiv:2204.09093v2 [cs.CV] UPDATED)

By and large, existing computational models of visual attention tacitly
assume perfect vision and full access to the stimulus and thereby deviate from
foveated biological vision. Moreover, modeling top-down attention is generally
reduced to the integration of semantic features without incorporating the
signal of a high-level visual tasks that have been shown to partially guide
human attention. We propose the Neural Visual Attention (NeVA) algorithm to
generate visual scanpaths in a top-down manner. With our method, we explore the
ability …

arxiv attention constraints human human-like machine networks neural networks visual attention