  Object-centric learning (OCL) extracts the representation of objects with
slots, offering an exceptional blend of flexibility and interpretability for
abstracting low-level perceptual features. A widely adopted method within OCL
is slot attention, which utilizes attention mechanisms to iteratively refine
slot representations. However, a major drawback of most object-centric models,
including slot attention, is their reliance on predefining the number of slots.
This not only necessitates prior knowledge of the dataset but also overlooks
the inherent variability in the number of objects present in each instance. To
overcome this fundamental limitation, we present a novel complexity-aware
object auto-encoder framework. Within this framework, we introduce an adaptive
slot attention (AdaSlot) mechanism that dynamically determines the optimal
number of slots based on the content of the data. This is achieved by proposing
a discrete slot sampling module that is responsible for selecting an
appropriate number of slots from a candidate list. Furthermore, we introduce a
masked slot decoder that suppresses unselected slots during the decoding
process. Our framework, tested extensively on object discovery tasks with
various datasets, shows performance matching or exceeding top fixed-slot
models. Moreover, our analysis substantiates that our method exhibits the
capability to dynamically adapt the slot number according to each instance's
complexity, offering the potential for further exploration in slot attention
research. Project will be available at https://kfan21.github.io/AdaSlot/
