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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 adoi:10.1109/cvpr.2014.456 dblp:conf/cvpr/ZhangWFHP14 fatcat:pbbsg5igpzc5lfx3fqx6drgvoe