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Most object-level mapping systems in use today make use of an upstreamlearned object instance segmentation model. If we want to teach them about anew object or segmentation class, we need to build a large dataset and retrainthe system. To build spatial AI systems that can quickly be taught about newobjects, we need to effectively solve the problem of single-shot objectdetection, instance segmentation and re-identification. So far there is neithera method fulfilling all of these requirements in unison nor a benchmark thatcould be used to test such a method. Addressing this, we propose ISAR, abenchmark and baseline method for single- and few-shot object InstanceSegmentation And Re-identification, in an effort to accelerate the developmentof algorithms that can robustly detect, segment, and re-identify objects from asingle or a few sparse training examples. We provide a semi-synthetic datasetof video sequences with ground-truth semantic annotations, a standardizedevaluation pipeline, and a baseline method. Our benchmark aligns with theemerging research trend of unifying Multi-Object Tracking, Video ObjectSegmentation, and Re-identification.

ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identification