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Christopher P. Burgess

3 accepted papers

2021

SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition

NeurIPS 2021poster

To help agents reason about scenes in terms of their building blocks, we wish to extract the compositional structure of any given scene (in particular, the configuration and characteristics of objects comprising the scene). This problem is especially difficult when scene structure needs to be inferr…

Cited by 82SourcePDFScholar
2021

Unsupervised Object-Based Transition Models For 3D Partially Observable Environments

NeurIPS 2021poster

We present a slot-wise, object-based transition model that decomposes a scene into objects, aligns them (with respect to a slot-wise object memory) to maintain a consistent order across time, and predicts how those objects evolve over successive frames. The model is trained end-to-end without superv…

Cited by 27SourcePDFScholar
2018

SCAN: Learning Hierarchical Compositional Visual Concepts

ICLR 2018poster

The seemingly infinite diversity of the natural world arises from a relatively small set of coherent rules, such as the laws of physics or chemistry. We conjecture that these rules give rise to regularities that can be discovered through primarily unsupervised experiences and represented as abstract…

Cited by 151SourcePDFScholar