MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised
  Learning of Motion and Content Features
Adrien Bardes Jean Ponce Yann LeCun
Self-supervised learning of visual representations has been focusing on
learning content features, which do not capture object motion or location, and
focus on identifying and differentiating objects in images and videos. On the
other hand, optical flow estimation is a task that does not involve
understanding the content of the images on which it is estimated. We unify the
two approaches and introduce MC-JEPA, a joint-embedding predictive architecture
and self-supervised learning approach to jointly learn optical flow and content
features within a shared encoder, demonstrating that the two associated
objectives; the optical flow estimation objective and the self-supervised
learning objective; benefit from each other and thus learn content features
that incorporate motion information. The proposed approach achieves performance
on-par with existing unsupervised optical flow benchmarks, as well as with
common self-supervised learning approaches on downstream tasks such as semantic
segmentation of images and videos.