  Most object-level mapping systems in use today make use of an upstream
learned object instance segmentation model. If we want to teach them about a
new object or segmentation class, we need to build a large dataset and retrain
the system. To build spatial AI systems that can quickly be taught about new
objects, we need to effectively solve the problem of single-shot object
detection, instance segmentation and re-identification. So far there is neither
a method fulfilling all of these requirements in unison nor a benchmark that
could be used to test such a method. Addressing this, we propose ISAR, a
benchmark and baseline method for single- and few-shot object Instance
Segmentation And Re-identification, in an effort to accelerate the development
of algorithms that can robustly detect, segment, and re-identify objects from a
single or a few sparse training examples. We provide a semi-synthetic dataset
of video sequences with ground-truth semantic annotations, a standardized
evaluation pipeline, and a baseline method. Our benchmark aligns with the
emerging research trend of unifying Multi-Object Tracking, Video Object
Segmentation, and Re-identification.

Computer Vision and Pattern Recognition;Robotics