Zero-Shot Object Recognition Using Semantic Label Vectors

We consider the problem of zero-shot recognition of object categories from images. Given a set of object categories (called "known classes") with training images, our goal is to learn a system to recognize another non-overlapping set of object categories (called "unknown classes") for which there are no training images. Our proposed approach exploits the recent work in natural language processing which has produced vector representations of words. Using the vector representations of object classes, we develop a method for transferring the appearance models from known object classes to unknown object classes. Our experimental results on three benchmark datasets show that our proposed method outperforms other competing approaches.