Scalable Zero-Shot Learning via Binary Visual-Semantic Embeddings

Zero-shot learning aims to classify the visual instances from unseen classes in the absence of training examples. This is typically achieved by directly mapping visual features to a semantic embedding space of classes (e.g., attributes or word vectors), where the similarity between the two modalitie...

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