Learning Discriminative Projection With Visual Semantic Alignment for Generalized Zero Shot Learning

Abstract

Zero Shot Learning (ZSL) aims to solve the classification problem with no training sample, and it is realized by transferring knowledge from source classes to target classes through the semantic embeddings bridging. Generalized ZSL (GZSL) enlarges the search scope of ZSL from only the seen classes to all classes. A large number of methods are proposed for these two settings, and achieve competing performance. However, most of them still suffer from the domain shift problem due to the existence of the domain gap between the seen classes and unseen classes. In this article, we propose a novel method to learn discriminative features with visual-semantic alignment for GZSL. We define a latent space, where the visual features and semantic attributes are aligned, and assume that each prototype is the linear combination of others, where the coefficients are constrained to be the same in all three spaces. To make the latent space more discriminative, a linear discriminative analysis strategy is employed to learn the projection matrix from visual space to latent space. Five popular datasets are exploited to evaluate the proposed method, and the results demonstrate the superiority of our approach compared with the state-of-the-art methods. Beside, extensive ablation studies also show the effectiveness of each module in our method.

Keywords

• Generalized zero-shot learning
• linear discriminative analysis
• visual semantic alignment
• prototype synthesis