  Unsupervised object-centric learning aims to represent the modular,
compositional, and causal structure of a scene as a set of object
representations and thereby promises to resolve many critical limitations of
traditional single-vector representations such as poor systematic
generalization. Although there have been many remarkable advances in recent
years, one of the most critical problems in this direction has been that
previous methods work only with simple and synthetic scenes but not with
complex and naturalistic images or videos. In this paper, we propose STEVE, an
unsupervised model for object-centric learning in videos. Our proposed model
makes a significant advancement by demonstrating its effectiveness on various
complex and naturalistic videos unprecedented in this line of research.
Interestingly, this is achieved by neither adding complexity to the model
architecture nor introducing a new objective or weak supervision. Rather, it is
achieved by a surprisingly simple architecture that uses a transformer-based
image decoder conditioned on slots and the learning objective is simply to
reconstruct the observation. Our experiment results on various complex and
naturalistic videos show significant improvements compared to the previous
state-of-the-art.
