Abstract:In zero-shot learning (ZSL), the task of recognizing unseen categories when no data for training is available, state-of-the-art methods generate visual features from semantic auxiliary information (e.g., attributes). In this work, we propose a valid alternative (simpler, yet better scoring) to fulfill the very same task. We observe that, if first-and second-order statistics of the classes to be recognized were known, sampling from Gaussian distributions would synthesize visual features that are almost identica… Show more
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