  The robust and efficient recognition of visual relations in images is a
hallmark of biological vision. We argue that, despite recent progress in visual
recognition, modern machine vision algorithms are severely limited in their
ability to learn visual relations. Through controlled experiments, we
demonstrate that visual-relation problems strain convolutional neural networks
(CNNs). The networks eventually break altogether when rote memorization becomes
impossible, as when intra-class variability exceeds network capacity. Motivated
by the comparable success of biological vision, we argue that feedback
mechanisms including attention and perceptual grouping may be the key
computational components underlying abstract visual reasoning.\
