Oisin Mac Aodha

Self-Supervised Learning of Semantically Consistent Visual Features via a Simple 3D Prior

This work introduces a new self-supervised approach that allows us to learn features for topologically complex object categories using a simple prior, and makes use of contrastive learning and distribution matching at the global dataset-level to learn the coarse shape and appearance of a category.