We introduce the task of spotting temporally precise, fine-grained events in
video (detecting the precise moment in time events occur). Precise spotting
requires models to reason globally about the full-time scale of actions and
locally to identify subtle frame-to-frame appearance and motion differences
that identify events during these actions. Surprisingly, we find that top
performing solutions to prior video understanding tasks such as action
detection and segmentation do not simultaneously meet both requirements. In
response, we propose E2E-Spot, a compact, end-to-end model that performs well
on the precise spotting task and can be trained quickly on a single GPU. We
demonstrate that E2E-Spot significantly outperforms recent baselines adapted
from the video action detection, segmentation, and spotting literature to the
precise spotting task. Finally, we contribute new annotations and splits to
several fine-grained sports action datasets to make these datasets suitable for
future work on precise spotting.

我们引入了一个具有精确定位时间、细粒度事件的任务（检测事件发生的确切时刻）。为了实现精确定位，模型需要全局推理活动的整个时间尺度，同时本地识别识别在这些活动期间标识事件的细微帧间外观和运动差异。我们提出了 E2E-Spot，它是一种紧凑的端到端模型，在精确定位任务上表现良好，并可以在单个 GPU 上快速训练。我们证明 E2E-Spot 显着优于最近从视频动作检测、分割和定位文献中调整的基线，用于精确定位任务。最后，我们对多个细粒度运动数据集进行了新的注释和拆分，使这些数据集适用于未来的精确定位研究。