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。

在视频中精确定位细粒度事件

Spotting Temporally Precise, Fine-Grained Events in Video

Considering event structure information has proven helpful in text-based
stock movement prediction. However, existing works mainly adopt the
coarse-grained events, which loses the specific semantic information of diverse
event types. In this work, we propose to incorporate the fine-grained events in
stock movement prediction. Firstly, we propose a professional finance event
dictionary built by domain experts and use it to extract fine-grained events
automatically from finance news. Then we design a neural model to combine
finance news with fine-grained event structure and stock trade data to predict
the stock movement. Besides, in order to improve the generalizability of the
proposed method, we design an advanced model that uses the extracted
fine-grained events as the distant supervised label to train a multi-task
framework of event extraction and stock prediction. The experimental results
show that our method outperforms all the baselines and has good
generalizability.

使用领域专家构建金融专业事件字典，并自动从金融新闻中提取精细的事件信息，并设计神经模型将金融新闻与精细的事件结构和股票交易数据相结合，预测股票运动，实验结果表明，该方法胜过所有基准线，并具有良好的泛化能力。