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Cross-modal hashing is designed to facilitate fast search across domains. In this work, we present a cross-modal hashing approach, called quantized correlation hashing (QCH), which takes into consideration the quantization loss over domains and the relation between domains. Unlike previous approaches that separate the optimization of the quantizer(More)
This paper presents a method of zero-shot learning (ZSL) which poses ZSL as the missing data problem, rather than the missing label problem. Specifically, most existing ZSL methods focus on learning mapping functions from the image feature space to the label embedding space. Whereas, the proposed method explores a simple yet effective transductive framework(More)
In the literature of cross-modal search, most methods employ linear models to pursue hash codes that preserve data similarity, in terms of Euclidean distance, both within-modal and across-modal. However, data dimensionality can be quite different across modalities. It is known that the behavior of Euclidean distance/similarity between datapoints can be(More)
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