  We present an approach for recommending a music track for a given video, and
vice versa, based on both their temporal alignment and their correspondence at
an artistic level. We propose a self-supervised approach that learns this
correspondence directly from data, without any need of human annotations. In
order to capture the high-level concepts that are required to solve the task,
we propose modeling the long-term temporal context of both the video and the
music signals, using Transformer networks for each modality. Experiments show
that this approach strongly outperforms alternatives that do not exploit the
temporal context. The combination of our contributions improve retrieval
accuracy up to 10x over prior state of the art. This strong improvement allows
us to introduce a wide range of analyses and applications. For instance, we can
condition music retrieval based on visually defined attributes.
