Improving semantic embedding consistency by metric learning for zero-shot classiffication

Abstract

This paper addresses the task of zero-shot image classification. The key contribution of the proposed approach is to control the semantic embedding of images - one of the main ingredients of zero-shot learning - by formulating it as a metric learning problem. The optimized empirical criterion associates two types of sub-task constraints: metric discriminating capacity and accurate attribute prediction. This results in a novel expression of zero-shot learning not requiring the notion of class in the training phase: only pairs of image/attributes, augmented with a consistency indicator, are given as ground truth. At test time, the learned model can predict the consistency of a test image with a given set of attributes, allowing flexible ways to produce recognition inferences. Despite its simplicity, the proposed approach gives state-ofthe-art results on four challenging datasets used for zero-shot recognition evaluation.