Enhancing Interpretable Object Abstraction via Clustering-based Slot
  Initialization
Ning Gao Bernard Hohmann Gerhard Neumann
Object-centric representations using slots have shown the advances towards
efficient, flexible and interpretable abstraction from low-level perceptual
features in a compositional scene. Current approaches randomize the initial
state of slots followed by an iterative refinement. As we show in this paper,
the random slot initialization significantly affects the accuracy of the final
slot prediction. Moreover, current approaches require a predetermined number of
slots from prior knowledge of the data, which limits the applicability in the
real world. In our work, we initialize the slot representations with clustering
algorithms conditioned on the perceptual input features. This requires an
additional layer in the architecture to initialize the slots given the
identified clusters. We design permutation invariant and permutation
equivariant versions of this layer to enable the exchangeable slot
representations after clustering. Additionally, we employ mean-shift clustering
to automatically identify the number of slots for a given scene. We evaluate
our method on object discovery and novel view synthesis tasks with various
datasets. The results show that our method outperforms prior works
consistently, especially for complex scenes.