SCALOR: Generative World Models with Scalable Object Representations

TL;DR

SCALOR is a probabilistic generative model that efficiently learns scalable, object-oriented representations of complex scenes with many objects and dynamic backgrounds in an unsupervised manner.

Contribution

It introduces spatially-parallel attention, proposal-rejection mechanisms, and a background module enabling scalable unsupervised scene understanding.

Findings

Handles scenes with up to 100 objects

Works on natural scenes with tens of moving objects

Models complex dynamic backgrounds

Abstract

Scalability in terms of object density in a scene is a primary challenge in unsupervised sequential object-oriented representation learning. Most of the previous models have been shown to work only on scenes with a few objects. In this paper, we propose SCALOR, a probabilistic generative world model for learning SCALable Object-oriented Representation of a video. With the proposed spatially-parallel attention and proposal-rejection mechanisms, SCALOR can deal with orders of magnitude larger numbers of objects compared to the previous state-of-the-art models. Additionally, we introduce a background module that allows SCALOR to model complex dynamic backgrounds as well as many foreground objects in the scene. We demonstrate that SCALOR can deal with crowded scenes containing up to a hundred objects while jointly modeling complex dynamic backgrounds. Importantly, SCALOR is the first unsupervised object representation model shown to work for natural scenes containing several tens of moving objects.