Current methods for action recognition primarily rely on deep convolutional
networks to derive feature embeddings of visual and motion features. While
these methods have demonstrated remarkable performance on standard benchmarks,
we are still in need of a better understanding as to how the videos, in
particular their internal structures, relate to high-level semantics, which may
lead to benefits in multiple aspects, e.g. interpretable predictions and even
new methods that can take the recognition performances to a next level. Towards
this goal, we construct TAPOS, a new dataset developed on sport videos with
manual annotations of sub-actions, and conduct a study on temporal action
parsing on top. Our study shows that a sport activity usually consists of
multiple sub-actions and that the awareness of such temporal structures is
beneficial to action recognition. We also investigate a number of temporal
parsing methods, and thereon devise an improved method that is capable of
mining sub-actions from training data without knowing the labels of them. On
the constructed TAPOS, the proposed method is shown to reveal intra-action
information, i.e. how action instances are made of sub-actions, and
inter-action information, i.e. one specific sub-action may commonly appear in
various actions.

使用 TAPOS 数据集，针对深度学习在运动识别中的限制，提出了一种改进的时序划分方法，能挖掘出运动过程中的子动作信息，从而有效提高识别性能。

通过时间动作解析实现内部和外部交互理解

Intra- and Inter-Action Understanding via Temporal Action Parsing

We present a method for weakly-supervised action localization based on graph
convolutions. In order to find and classify video time segments that correspond
to relevant action classes, a system must be able to both identify
discriminative time segments in each video, and identify the full extent of
each action. Achieving this with weak video level labels requires the system to
use similarity and dissimilarity between moments across videos in the training
data to understand both how an action appears, as well as the sub-actions that
comprise the action's full extent. However, current methods do not make
explicit use of similarity between video moments to inform the localization and
classification predictions. We present a novel method that uses graph
convolutions to explicitly model similarity between video moments. Our method
utilizes similarity graphs that encode appearance and motion, and pushes the
state of the art on THUMOS '14, ActivityNet 1.2, and Charades for weakly
supervised action localization.

本文提出了一种基于图卷积的弱监督动作定位方法，使用相似性图形显式建模视频片段之间的相似性以及动作的子操作，最终在多个数据集上达到了弱监督动作定位的最新水平。