Dual-Contrastive Attribute Embedding for Generalized Zero-Shot Learning

Zero-shot learning (ZSL) aims to categorize target classes with the aid of semantic knowledge and samples from previously seen classes. In this process, the alignment of visual and attribute modality features is key to successful knowledge transfer. Several previous studies have investigated the extraction of attribute-related local features to reduce visual-semantic domain gaps and overcome issues with domain shifts. However, these techniques do not emphasize the commonality of features across different objects belonging to the same attribute, which is critical for identifying and distinguishing the attributes of unseen classes. In this study, we propose a novel ZSL method, termed dual-contrastive attribute embedding (DCAE), for generalized zero-shot learning. This approach simultaneously learns both class-level and attribute-level prototypes and representations. Specifically, an attribute embedding module is introduced to capture attribute-level features and an attribute semantic encoder is developed to generate attribute prototypes. Attribute-level and class-level contrastive loss terms are then used to optimize an attribute embedding space such that attribute features are compactly distributed around corresponding prototypes. This double contrastive learning mechanism facilitates the alignment of multimodal information from two dimensions. Extensive experiments with three benchmark datasets demonstrated the superiority of the proposed method compared to current state-of-the-art techniques.