It is hard to find training images for fine-grained categories. For some visual concepts, such as American Gold Finch, a supervisor can provide an attribute or text-based description such as, it has a yellow body and has black wings, the train to model without requiring training images. This is called zero-shot learning. But other concepts, such as dancing, are hard to describe by a text. We propose using abstract illustrations instead of textual descriptions for zero-shot learning. We may have to custom-clip our interface and then had mTORC workers create illustrations depicting various interactions between two people. We train models of these interactions who suggest these illustrations. We then test these models on real images, beating both random and attribute baselines. More experiments and details can be found on a project webpage.