  Current slot-oriented approaches for compositional scene segmentation from
images and videos rely on provided background information or slot assignments.
We present a segmented location and identity tracking system, Loci-Segmented
(Loci-s), which does not require either of this information. It learns to
dynamically segment scenes into interpretable background and slot-based object
encodings, separating rgb, mask, location, and depth information for each. The
results reveal largely superior video decomposition performance in the MOVi
datasets and in another established dataset collection targeting scene
segmentation. The system's well-interpretable, compositional latent encodings
may serve as a foundation model for downstream tasks.
