Embarrassingly Easy Zero-Shot Image Recognition

Zero-shot Learning (ZSL) aims to transfer knowledge from seen image categories to unseen ones by leveraging semantic information. It is generally assumed that the seen and unseen classes share a common semantic space. A number of methods propose to design a common space to accomplish the projection between image and class embeddings by learning a compatibility function, which make up sample pairs to train the object function. However, considering the drawbacks of previous compatibility function, we design a new compatibility function in this paper. Different from previous compatibility pattern, our proposed compatibility function is more discriminative by employing label vectors, which can measure the similarity between the projected image features and all seen class prototypes. Extensive experiments on four benchmark datasets show the effectiveness of our proposed approach.