Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classiffication


This paper addresses the task of zero-shot image classification. The key contribution of the proposed approach is to control the semantic embedding of images – one of the main ingredients of zero-shot learning – by formulating it as a metric learning problem. The optimized empirical criterion associates two types of sub-task constraints: metric… (More)
DOI: 10.1007/978-3-319-46454-1_44


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