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ShapeY: Measuring Shape Recognition Capacity Using Nearest Neighbor Matching

2021-11-16 01:21:54
Jong Woo Nam, Amanda S. Rios, Bartlett W. Mel

Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2111.08174

PDF

https://arxiv.org/pdf/2111.08174.pdf


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