Discriminative comparison classifier for generalized zero-shot learning

Comparison classifier based generalized zero-shot learning (GZSL) helps to achieve knowledge transfer from seen to unseen classes. However, it only utilizes the original semantic features, which are highly related and indistinguishable, to learn an embedding with no consideration of the relationship between them. Moreover, it cannot well encode discriminative information embedded in semantic features. To handle these problems, we present a discriminative comparison classifier for GZSL, which consists of semantic embedding network and relation network. Semantic embedding network takes as input original semantic features and relationship features which can be obtained by clustering, it ensures that the embedding network from semantic space to visual space can learn more discriminative features. Relation network is used to learn relationship between the embedded features and visual features, the validation information will guide embedding network to learn more discriminate features. Moreover, we adopt a novel semantic pivot regularization to keep inter-class discrimination in the visual space. Extensive experiments on several real-world datasets demonstrate the effectiveness of our method over the other state-of-the-arts. (C) 2020 Elsevier B.V. All rights reserved.