A Computational Account Of Self-Supervised Visual Learning From
  Egocentric Object Play
Deepayan Sanyal Joel Michelson Yuan Yang James Ainooson Maithilee Kunda
Research in child development has shown that embodied experience handling
physical objects contributes to many cognitive abilities, including visual
learning. One characteristic of such experience is that the learner sees the
same object from several different viewpoints. In this paper, we study how
learning signals that equate different viewpoints -- e.g., assigning similar
representations to different views of a single object -- can support robust
visual learning. We use the Toybox dataset, which contains egocentric videos of
humans manipulating different objects, and conduct experiments using a computer
vision framework for self-supervised contrastive learning. We find that
representations learned by equating different physical viewpoints of an object
benefit downstream image classification accuracy. Further experiments show that
this performance improvement is robust to variations in the gaps between
viewpoints, and that the benefits transfer to several different image
classification tasks.