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In this paper, we tackle the zero-shot learning (ZSL) classification problem and analyse one of its key ingredients, the semantic embedding. Despite their fundamental role, semantic embeddings are not learnt from the visual data to be classified, but, instead, they either come from manual annotation (attributes) or from a linguistic text corpus (distributed word embeddings, DWEs). Hence, there is no guarantee that visual and semantic information could fit well, and as to bridge this gap, wedblp:conf/bmvc/RoyCM18 fatcat:ewp67goupvco7jaunnfyzgwv2q