  We propose topology-aware feature partitioning into $k$ disjoint partitions
for given scene features as a method for object-centric representation
learning. To this end, we propose to use minimum $s$-$t$ graph cuts as a
partitioning method which is represented as a linear program. The method is
topologically aware since it explicitly encodes neighborhood relationships in
the image graph. To solve the graph cuts our solution relies on an efficient,
scalable, and differentiable quadratic programming approximation. Optimizations
specific to cut problems allow us to solve the quadratic programs and compute
their gradients significantly more efficiently compared with the general
quadratic programming approach. Our results show that our approach is scalable
and outperforms existing methods on object discovery tasks with textured scenes
and objects.
