  Our objective is to transform a video into a set of discrete audio-visual
objects using self-supervised learning. To this end, we introduce a model that
uses attention to localize and group sound sources, and optical flow to
aggregate information over time. We demonstrate the effectiveness of the
audio-visual object embeddings that our model learns by using them for four
downstream speech-oriented tasks: (a) multi-speaker sound source separation,
(b) localizing and tracking speakers, (c) correcting misaligned audio-visual
data, and (d) active speaker detection. Using our representation, these tasks
can be solved entirely by training on unlabeled video, without the aid of
object detectors. We also demonstrate the generality of our method by applying
it to non-human speakers, including cartoons and puppets.Our model
significantly outperforms other self-supervised approaches, and obtains
performance competitive with methods that use supervised face detection.
