  Object recognition in humans depends primarily on shape cues. We have
developed a new approach to measuring the shape recognition performance of a
vision system based on nearest neighbor view matching within the system's
embedding space. Our performance benchmark, ShapeY, allows for precise control
of task difficulty, by enforcing that view matching span a specified degree of
3D viewpoint change and/or appearance change. As a first test case we measured
the performance of ResNet50 pre-trained on ImageNet. Matching error rates were
high. For example, a 27 degree change in object pitch led ResNet50 to match the
incorrect object 45% of the time. Appearance changes were also highly
disruptive. Examination of false matches indicates that ResNet50's embedding
space is severely "tangled". These findings suggest ShapeY can be a useful tool
for charting the progress of artificial vision systems towards human-level
shape recognition capabilities.
