ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identification
| Autor: | Gorlo, Nicolas, Blomqvist, Kenneth, Milano, Francesco, Siegwart, Roland |
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| Rok vydání: | 2023 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 8 pages, 6 figures, to be published in IEEE WACV 2024 |
| Databáze: | arXiv |
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