Current developments in temporal event or action localization usually target
actions captured by a single camera. However, extensive events or actions in
the wild may be captured as a sequence of shots by multiple cameras at
different positions. In this paper, we propose a new and challenging task
called multi-shot temporal event localization, and accordingly, collect a large
scale dataset called MUlti-Shot EventS (MUSES). MUSES has 31,477 event
instances for a total of 716 video hours. The core nature of MUSES is the
frequent shot cuts, for an average of 19 shots per instance and 176 shots per
video, which induces large intrainstance variations. Our comprehensive
evaluations show that the state-of-the-art method in temporal action
localization only achieves an mAP of 13.1% at IoU=0.5. As a minor contribution,
we present a simple baseline approach for handling the intra-instance
variations, which reports an mAP of 18.9% on MUSES and 56.9% on THUMOS14 at
IoU=0.5. To facilitate research in this direction, we release the dataset and
the project code at this https URL .

本文提出了一个新的挑战性任务，即多镜头时间事件定位，并相应地收集了一个名为 MUSES 的大规模数据集，其中包含 31,477 个事件实例，共计 716 个小时的视频；同时，我们在处理内部实例差异方面提出了一个简单的基线方法，并在 IoU=0.5 的情况下，在 MUSES 和 THUMOS14 上报告了分别为 18.9％和 56.9％的 mAP 值。