Zero-Shot Learning for Cross-Domain Image Classification Using Semantic Embeddings
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Abstract
Traditional image classification models rely heavily on large labeled datasets, limiting their applicability in domains with scarce data. This paper explores zero-shot learning (ZSL) techniques that enable models to classify images from unseen categories by leveraging semantic embeddings. A hybrid framework combining visual features from convolutional neural networks (CNNs) and semantic embeddings from word vectors is proposed. Experiments on benchmark datasets demonstrate that the approach achieves competitive performance in cross-domain classification tasks, paving the way for scalable and data-efficient AI solutions.
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