Title: Human perception in computer vision,
Abstract: Computer vision has made remarkable progress in recent years. Deep neural
network (DNN) models optimized to identify objects in images exhibit
unprecedented task-trained accuracy and, remarkably, some generalization
ability: new visual problems can now be solved more easily based on previous
learning. Biological vision (learned in life and through evolution) is also
accurate and general-purpose. Is it possible that these different learning
regimes converge to similar problem-dependent optimal computations? We
therefore asked whether the human system-level computation of visual perception
has DNN correlates and considered several anecdotal test cases. We found that
perceptual sensitivity to image changes has DNN mid-computation correlates,
while sensitivity to segmentation, crowding and shape has DNN end-computation
correlates. Our results quantify the applicability of using DNN computation to
estimate perceptual loss, and are consistent with the fascinating theoretical
view that properties of human perception are a consequence of
architecture-independent visual learning.