Predicting Failures of Vision Systems

Peng Zhang

Abstract:

Computer vision systems today fail frequently. They also fail abruptly without warning or explanation. Alleviating
the former has been the primary focus of the community. In this work, we hope to draw the community’s attention to the
latter, which is arguably equally problematic for real applications. We promote two metrics to evaluate failure prediction. We show that a surprisingly straightforward and general approach, that we call ALERT, can predict the likely
accuracy (or failure) of a variety of computer vision systems – semantic segmentation, vanishing point and camera
parameter estimation, and image memorability prediction on individual input images. We also explore attribute prediction, where classifiers are typically meant to generalize to new unseen categories. We show that ALERT can be useful in predicting failures of this transfer. Finally, we leverage ALERT to improve the performance of a downstream application of attribute prediction: zero-shot learning. We show that ALERT can outperform several strong baselines for zero-shot learning on four datasets