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

This is the accompanying video of our WACV 2024 paper "ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identification".

Paper: https://arxiv.org/abs/2311.02734
Project page: https://nicogorlo.github.io/isar_wacv24
Code: https://github.com/nicogorlo/isar

Abstract:
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.

Reference:
Nicolas Gorlo, Kenneth Blomqvist, Francesco Milano and Roland Siegwart, "ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identification", IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024.

Affiliations:
Autonomous Systems Laboratory, ETH Zurich, Switzerland.