DUET: Cross-Modal Semantic Grounding for Contrastive Zero-Shot Learning

Zero-shot learning (ZSL) aims to predict unseen classes whose samples have never appeared during training. As annotations for class-level visual characteristics, attributes are widely used semantic information for zero-shot image classification. However, the current methods often fail to discriminate those subtle visual distinctions between images due to not only the lack of fine-grained annotations, but also the issues of attribute imbalance and co-occurrence. In this paper, we present a transformer-based end-to-end ZSL method named DUET, which integrates latent semantic knowledge from the pre-trained language models (PLMs) via a self-supervised multi-modal learning paradigm. Specifically, we (1) developed a cross-modal semantic grounding network to investigate the model's capability of disentangling semantic attributes from the images; (2) applied an attribute-level contrastive learning strategy to further enhance the model's discrimination on fine-grained visual characteristics against the attribute co-occurrence and imbalance; (3) proposed a multi-task learning policy for considering multi-model objectives. We find that DUET can achieve state-of-the-art performance on three standard ZSL benchmarks and a knowledge graph equipped ZSL benchmark, and that its components are effective and its predictions are interpretable.