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Systems that can associate images with their spoken audio captions are animportant step towards visually grounded language learning. We describe ascalable method to automatically generate diverse audio for image captioningdatasets. This supports pretraining deep networks for encoding both audio andimages, which we do via a dual encoder that learns to align latentrepresentations from both modalities. We show that a masked margin softmax lossfor such models is superior to the standard triplet loss. We fine-tune thesemodels on the Flickr8k Audio Captions Corpus and obtain state-of-the-artresults---improving recall in the top 10 from 29.6% to 49.5%. We also obtainhuman ratings on retrieval outputs to better assess the impact of incidentallymatching image-caption pairs that were not associated in the data, finding thatautomatic evaluation substantially underestimates the quality of the retrievedresults.

Large-scale representation learning from visually grounded untranscribed speech