Grounded Object Centric Learning

Understand

The extraction of modular object-centric representations for downstream tasks is an emerging area of research.

• Learning grounded representations of objects that are guaranteed to be stable and invariant promises robust performance across different tasks and environments.
• Slot Attention (SA) learns object-centric representations by assigning objects to \textit{slots}, but presupposes a \textit{single} distribution from which all slots are randomly initialised.
• This results in an inability to learn \textit{specialized} slots which bind to specific object types and remain invariant to identity-preserving changes in object appearance.