Grounded Object Centric Learning
arXiv (Cornell University)(2023)
摘要
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 slots, but
presupposes a single distribution from which all slots are randomly
initialised. This results in an inability to learn specialized slots
which bind to specific object types and remain invariant to identity-preserving
changes in object appearance. To address this, we present
Conditional Slot Attention (CoSA)
using a novel concept of Grounded Slot Dictionary (GSD) inspired by
vector quantization. Our proposed GSD comprises (i) canonical object-level
property vectors and (ii) parametric Gaussian distributions, which define a
prior over the slots. We demonstrate the benefits of our method in multiple
downstream tasks such as scene generation, composition, and task adaptation,
whilst remaining competitive with SA in popular object discovery benchmarks.
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关键词
object centric learning,attention
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