From "Umwelt" to "World" models

Objects play an important role in vision. Much of human vision is centered around objects and there is evidence we develop a basic understanding of what objects are from a very young age.
Learning about objects without supervision has been a focus of much research in recent years. Many models have been suggested with different structures, assumptions and inductive biases
and while impressive progress has been achieved in some limited domains we have yet to obtain a general system that can learn about objects unsupervised from real-world data.

In this talk I argue that there is a fundamental mismatch between some of the assumptions made by most "object centric" models and real-world data, and that this mismatch prevents such models from learning
meaningful representations at scale - ultimately making the problem setting ill-posed. Following that I will present some of our current work which attempts to address some of these issues.