It is well-known that modern computer vision systems often exhibit behaviors
misaligned with those of humans: from adversarial attacks to image corruptions,
deep learning vision models suffer in a variety of settings that humans capably
handle. In light of these phenomena, here we introduce another, orthogonal
perspective studying the human-machine vision gap. We revisit the task of
recovering images under degradation, first introduced over 30 years ago in the
Recognition-by-Components theory of human vision. Specifically, we study the
performance and behavior of neural networks on the seemingly simple task of
classifying regular polygons at varying orders of degradation along their
perimeters. To this end, we implement the Automated Shape Recoverability Test
for rapidly generating large-scale datasets of perimeter-degraded regular
polygons, modernizing the historically manual creation of image recoverability
experiments. We then investigate the capacity of neural networks to recognize
and recover such degraded shapes when initialized with different priors.
Ultimately, we find that neural networks' behavior on this simple task
conflicts with human behavior, raising a fundamental question of the robustness
and learning capabilities of modern computer vision models.

研究了现代计算机视觉系统与人类视觉存在的差距，通过测试神经网络对多变形正多边形边缘降级的图像的分类和恢复能力，发现神经网络的行为与人类行为存在冲突，引发了对现代计算机视觉模型的鲁棒性和学习能力的根本质疑。