  Slot attention has shown remarkable object-centric representation learning
performance in computer vision tasks without requiring any supervision. Despite
its object-centric binding ability brought by compositional modelling, as a
deterministic module, slot attention lacks the ability to generate novel
scenes. In this paper, we propose the Slot-VAE, a generative model that
integrates slot attention with the hierarchical VAE framework for
object-centric structured scene generation. For each image, the model
simultaneously infers a global scene representation to capture high-level scene
structure and object-centric slot representations to embed individual object
components. During generation, slot representations are generated from the
global scene representation to ensure coherent scene structures. Our extensive
evaluation of the scene generation ability indicates that Slot-VAE outperforms
slot representation-based generative baselines in terms of sample quality and
scene structure accuracy.
