An Inverse Mapping with Manifold Alignment for Zero-Shot Learning

Zero-shot learning aims to recognize objects from unseen classes, where samples are not available at the training stage, by transferring knowledge from seen classes, where labeled samples are provided. It bridges seen and unseen classes via a shared semantic space such as class attribute space or class prototype space. While previous approaches have tried to learning a mapping function from the visual space to the semantic space with different objective functions, we take a different approach and try to map from the semantic space to the visual space. The inverse mapping predicts the visual feature prototype of each unseen class via the semantic vector for image classification. We also propose a heuristic algorithm to select a high density set from data of each seen class. The visual feature prototypes from the high density sets are more discriminative, which is benefit to the classification. Our approach is evaluated for zero-shot recognition on four benchmark data sets and significantly outperforms the state-of-the-art methods on AWA, SUN, APY.