Prototype Relaxation With Robust Principal Component Analysis for Zero Shot Learning

Zero Shot Learning (ZSL) has been attracting increasing attention due to its powerful ability of recognizing objects of unseen classes. As one type of ZSL methods, the low rank based strategy has achieved remarkable success. However, traditional low rank based methods are often based on the assumption that a variety of visual features from a same class can be projected to a single attribute by ignoring the background information and other noisy interference in visual features. This assumption is unreasonable and often leads to bad performance when there is big variance within a class. In this paper, a novel method called Prototype Relaxation with Robust Principal Component Analysis (RPCA) is proposed to relax this assumption by adding a sparse noise constraint. In addition, to avoid the confusion between similar classes, an orthogonal constraint is employed to disperse all the class prototypes, including both seen and unseen classes, in latent space. Furthermore, to alleviate the domain shift problem, vectors from latent space are exploited to reconstruct visual features and semantic attributes respectively. Besides, the hubness problem is also mitigated by applying the max probability model in all three spaces. Extensive experiments are conducted on four popular datasets and the results demonstrate the superiority of this method.