  Segmentation and tracking of unseen object instances in discrete frames pose
a significant challenge in dynamic industrial robotic contexts, such as
distribution warehouses. Here, robots must handle object rearrangement,
including shifting, removal, and partial occlusion by new items, and track
these items after substantial temporal gaps. The task is further complicated
when robots encounter objects not learned in their training sets, which
requires the ability to segment and track previously unseen items. Considering
that continuous observation is often inaccessible in such settings, our task
involves working with a discrete set of frames separated by indefinite periods
during which substantial changes to the scene may occur. This task also
translates to domestic robotic applications, such as rearrangement of objects
on a table. To address these demanding challenges, we introduce new synthetic
and real-world datasets that replicate these industrial and household
scenarios. We also propose a novel paradigm for joint segmentation and tracking
in discrete frames along with a transformer module that facilitates efficient
inter-frame communication. The experiments we conduct show that our approach
significantly outperforms recent methods. For additional results and videos,
please visit \href{https://sites.google.com/view/stow-corl23}{website}. Code
and dataset will be released.
