Learning Visually Consistent Label Embeddings for Zero-Shot Learning

16 May 2019Berkan DemirelRamazan Gokberk CinbisNazli Ikizler-Cinbis

In this work, we propose a zero-shot learning method to effectively model knowledge transfer between classes via jointly learning visually consistent word vectors and label embedding model in an end-to-end manner. The main idea is to project the vector space word vectors of attributes and classes into the visual space such that word representations of semantically related classes become more closer, and use the projected vectors in the proposed embedding model to identify unseen classes... (read more)

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