Zero-Shot Learning with Structured Embeddings

Despite significant recent advances in image classification, fine-grained classifi-cation remains a challenge. In the present paper, we address the zero-shot and few-shot learning scenarios as obtaining labeled data is especially difficult for fine-grained classification tasks. First, we embed state-of-the-art image descrip-tors in a label embedding space using side information such as attributes. We argue that learning a joint embedding space, that maximizes the compatibility between the input and output embeddings, is highly effective for zero/few-shot learning. We show empirically that such embeddings significantly outperforms the current state-of-the-art methods on two challenging datasets (Caltech-UCSD Birds and Animals with Attributes). Second, to reduce the amount of costly manual at-tribute annotations, we use alternate output embeddings based on the word-vector representations, obtained from large text-corpora without any supervision. We re-port that such unsupervised embeddings achieve encouraging results, and lead to further improvements when combined with the supervised ones.