  In this paper, we explore neural network models that learn to associate
segments of spoken audio captions with the semantically relevant portions of
natural images that they refer to. We demonstrate that these audio-visual
associative localizations emerge from network-internal representations learned
as a by-product of training to perform an image-audio retrieval task. Our
models operate directly on the image pixels and speech waveform, and do not
rely on any conventional supervision in the form of labels, segmentations, or
alignments between the modalities during training. We perform analysis using
the Places 205 and ADE20k datasets demonstrating that our models implicitly
learn semantically-coupled object and word detectors.
