Abstract
One category-invariant methodology for object identification is zero-shot learning (ZSL), which uses semantic embeddings to categorize unseen categories. The ZSL method is indispensable in fields where data is scarce, including medical diagnosis and navigation. This paper proposes an Improved ZSL (I-ZSL) framework to increase the object recognition accuracy and generalization for the medical and navigation applications. The proposed framework is a hybrid architecture that uses Variational Autoencoders (VAEs) for robust feature generation and Transformer-based embeddings for semantic alignment. A domain-adaptive classifier, trained through contrastive learning, bridges the gap between the seen and unseen classes. The classifier has identified the framework with minimal training data in medical diagnostics for rare disease diagnosis. The proposed I-ZSL framework achieved a 20% improvement in F1-score over state-of-the-art models. In navigation, it demonstrated 25% better performance in novel landmark recognition under dynamic environmental conditions. These results show the framework's efficiency in addressing domain-specific challenges. This work presents ZSL with great potential to further object recognition in applications with significant impacts.
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