Towards Transparent Systems: Semantic Characterization Of Failure Modes

Today's computer vision systems are not perfect. They fail frequently. Even worse, they fail abruptly and seemingly inexplicably. We argue that making our systems more transparent via an explicit human understandable characterization of their failure modes is desirable. We propose characterizing the failure modes of a vision system using semantic attributes. For example, a face recognition system may say "If the test image is blurry, or the face is not frontal, or the person to be recognized is a young white woman with heavy make up, I am likely to fail." This information can be used at training time by researchers to design better features, models or collect more focused training data. It can also be used by a downstream machine or human user at test time to know when to ignore the output of the system, in turn making it more reliable. To generate such a "specification sheet", we discriminatively cluster incorrectly classified images in the semantic attribute space using L1-regularized weighted logistic regression. We show that our specification sheets can predict oncoming failures for face and animal species recognition better than several strong baselines. We also show that lay people can easily follow our specification sheets.