Online action detection, which aims to identify an ongoing action from a
streaming video, is an important subject in real-world applications. For this
task, previous methods use recurrent neural networks for modeling temporal
relations in an input sequence. However, these methods overlook the fact that
the input image sequence includes not only the action of interest but
background and irrelevant actions. This would induce recurrent units to
accumulate unnecessary information for encoding features on the action of
interest. To overcome this problem, we propose a novel recurrent unit, named
Information Discrimination Unit (IDU), which explicitly discriminates the
information relevancy between an ongoing action and others to decide whether to
accumulate the input information. This enables learning more discriminative
representations for identifying an ongoing action. In this paper, we further
present a new recurrent unit, called Information Integration Unit (IIU), for
action anticipation. Our IIU exploits the outputs from IDU as pseudo action
labels as well as RGB frames to learn enriched features of observed actions
effectively. In experiments on TVSeries and THUMOS-14, the proposed methods
outperform state-of-the-art methods by a significant margin in online action
detection and action anticipation. Moreover, we demonstrate the effectiveness
of the proposed units by conducting comprehensive ablation studies.

该研究提出了名为 Information Discrimination Unit（IDU）的循环单元以区分背景信息和感兴趣的动作信息，同时还提出了名为 Information Integration Unit（IIU）的循环单元用于动作预测，并在 TVSeries 和 THUMOS-14 数据集上取得了显著的性能提升。

在线行动检测的信息辨析学习：分析与应用

Learning to Discriminate Information for Online Action Detection:  Analysis and Application

From a streaming video, online action detection aims to identify actions in
the present. For this task, previous methods use recurrent networks to model
the temporal sequence of current action frames. However, these methods overlook
the fact that an input image sequence includes background and irrelevant
actions as well as the action of interest. For online action detection, in this
paper, we propose a novel recurrent unit to explicitly discriminate the
information relevant to an ongoing action from others. Our unit, named
Information Discrimination Unit (IDU), decides whether to accumulate input
information based on its relevance to the current action. This enables our
recurrent network with IDU to learn a more discriminative representation for
identifying ongoing actions. In experiments on two benchmark datasets, TVSeries
and THUMOS-14, the proposed method outperforms state-of-the-art methods by a
significant margin. Moreover, we demonstrate the effectiveness of our recurrent
unit by conducting comprehensive ablation studies.

介绍了一种在线行动检测方法，使用名为 Information Discrimination Unit（IDU）的新型循环单元来显式区分与当前操作相关的信息和其他信息，以学习更具辨别力的表示来鉴定当前操作，实验证明该方法在 TVSeries 和 THUMOS-14 两个基准数据集上的效果显著优于现有方法，并通过广泛的消融实验证明了循环单元的有效性。