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Zero-shot learning by exploiting class-related and attribute-related prior knowledge

Zero-shot learning by exploiting class-related and attribute-related prior knowledge

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The existing attribute-based zero-shot learning models at different levels ignore some necessary prior knowledge. It is essential to improve classification accuracy of zero-shot learning that how to mine attribute-related and class-related prior knowledge further being incorporated into the attribute prediction models. For the mining of class-related prior knowledge, measurement of the class–class correlation by using whitened cosine similarity is proposed. Likewise for the mining of attribute-related prior knowledge, measurements of the attribute–class and attribute–attribute correlation are proposed by using sparse representation coefficient. Therefore, a novel indirect attribute prediction (IAP) model is presented by exploiting class-related and attribute-related prior knowledge (IAP_CAPK). Experimental results on animals with attributes and a-Pascal/a-Yahoo datasets show that, when compared with IAP and direct attribute prediction, the proposed IAP_CAPK not only yields more accurate attribute prediction and zero-shot image classification, but also achieves much higher computational efficiency.

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