Learning unseen visual prototypes for zero-shot classification.

•A novel zero-shot learning method is developed to rectify the hubness and domain shift problem.•The proposed method exploits the class level visual samples to learn the projection function.•The unseen visual prototypes are modified by the label correlations and their knns.•The proposed method outperforms existing methods on 5 zero-shot learning datasets.