Abstract

The performance of binary classifification tasks, such as action classifification and object detection, is often measured in terms of the average precision (AP). Yet it is common practice in computer vision to employ the support vector machine (SVM) classififier, which optimizes a surrogate 0-1 loss. The popularity of SVM can be attributed to its empirical performance. Specififically, in fully supervised settings, SVM tends to provide similar accuracy to the AP-SVM classififier, which directly optimizes an AP-based loss. However, we hypothesize that in the signifificantly more challenging and practically useful setting of weakly supervised learning, it becomes crucial to optimize the right accuracy measure. In order to test this hypothesis, we propose a novel latent AP-SVM that minimizes a carefully designed upper bound on the AP-based loss function over weakly supervised samples. Using publicly available datasets, we demonstrate the advantage of our approach over standard loss-based binary classififiers on two challenging problems: action classifification and character recognition