【推荐理由】本文提出一种统一且可扩展的多对象视频跟踪器，利用图神经网络来处理层次结构的所有层级，使该模型在时间尺度上是统一的且非常通用。
Unifying Short and Long-Term Tracking with Graph Hierarchies
Orcun Cetintas，Guillem Braso´ ，Laura Leal-Taixe´
[Technical University of Munich & Munich Center for Machine Learning]
【论文链接】https://arxiv.org/pdf/2212.03038.pdf
【项目链接】https://bit.ly/sushi-mot
【摘要】高效地跟踪长视频中的对象意味着需要解决一系列问题，从为未被遮挡的物体进行短期关联到为遮挡后重新出现在场景中的物体进行长期关联。处理这些任务的方法通常是不相关的并且专门针对特定情况设计的，而表现最佳的方法通常是各种技术的混合，这导致工程密集的解决方案缺乏普适性。在本项工作中，作者质疑混合方法的必要性，并引入了SUSHI，一种统一且可扩展的多对象跟踪器。该方法通过将长视频剪辑成一个子剪辑层次结构来处理长剪辑，从而实现高度可扩展性。文章利用图神经网络来处理层次结构的所有层级，使该模型在时间尺度上是统一的且非常通用。因此，在四个不同的数据集上，该方法得到了显著的改进，超越了现有技术的水平。
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