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Most of us are not experts in specific fields, such as ornithology.Nonetheless, we do have general image and language understanding capabilitiesthat we use to match what we see to expert resources. This allows us to expandour knowledge and perform novel tasks without ad-hoc external supervision. Onthe contrary, machines have a much harder time consulting expert-curatedknowledge bases unless trained specifically with that knowledge in mind. Thus,in this paper we consider a new problem: fine-grained image recognition withoutexpert annotations, which we address by leveraging the vast knowledge availablein web encyclopedias. First, we learn a model to describe the visual appearanceof objects using non-expert image descriptions. We then train a fine-grainedtextual similarity model that matches image descriptions with documents on asentence-level basis. We evaluate the method on two datasets and compare withseveral strong baselines and the state of the art in cross-modal retrieval.Code is available at: https://github.com/subhc/clever

The Curious Layperson: Fine-Grained Image Recognition without Expert Labels