  The ability to decompose complex natural scenes into meaningful
object-centric abstractions lies at the core of human perception and reasoning.
In the recent culmination of unsupervised object-centric learning, the
Slot-Attention module has played an important role with its simple yet
effective design and fostered many powerful variants. These methods, however,
have been exceedingly difficult to train without supervision and are ambiguous
in the notion of object, especially for complex natural scenes. In this paper,
we propose to address these issues by investigating the potential of learnable
queries as initializations for Slot-Attention learning, uniting it with efforts
from existing attempts on improving Slot-Attention learning with bi-level
optimization. With simple code adjustments on Slot-Attention, our model,
Bi-level Optimized Query Slot Attention, achieves state-of-the-art results on 3
challenging synthetic and 7 complex real-world datasets in unsupervised image
segmentation and reconstruction, outperforming previous baselines by a large
margin. We provide thorough ablative studies to validate the necessity and
effectiveness of our design. Additionally, our model exhibits great potential
for concept binding and zero-shot learning. Our work is made publicly available
at https://bo-qsa.github.io
