Among the existing modalities for 3D action recognition, 3D flow has been
poorly examined, although conveying rich motion information cues for human
actions. Presumably, its susceptibility to noise renders it intractable, thus
challenging the learning process within deep models. This work demonstrates the
use of 3D flow sequence by a deep spatiotemporal model and further proposes an
incremental two-level spatial attention mechanism, guided from skeleton domain,
for emphasizing motion features close to the body joint areas and according to
their informativeness. Towards this end, an extended deep skeleton model is
also introduced to learn the most discriminant action motion dynamics, so as to
estimate an informativeness score for each joint. Subsequently, a late fusion
scheme is adopted between the two models for learning the high level
cross-modal correlations. Experimental results on the currently largest and
most challenging dataset NTU RGB+D, demonstrate the effectiveness of the
proposed approach, achieving state-of-the-art results.

本研究提出了一种基于 3D 流的增量二级空间注意机制，通过骨骼域引导，强调靠近身体关节区域的运动特征及其信息性，并提出了扩展的深度骨骼模型，用于学习最具有判别力的动作运动动力学，从而评估每个关节的信息性得分，并采用两个模型之间的后期融合方案来学习高水平的跨模态相关性，实验结果在当前最大和最具挑战性的数据集 NTU RGB+D 上表明，所提出的方法的有效性达到了最先进的水平。

利用骨架指导的学习场景流进行三维动作识别

Learning Scene Flow With Skeleton Guidance For 3D Action Recognition

In recent years, with the increase of social investment in scientific
research, the number of research results in various fields has increased
significantly. Accurately and effectively predicting the trends of future
research topics can help researchers discover future research hotspots.
However, due to the increasingly close correlation between various research
themes, there is a certain dependency relationship between a large number of
research themes. Viewing a single research theme in isolation and using
traditional sequence problem processing methods cannot effectively explore the
spatial dependencies between these research themes. To simultaneously capture
the spatial dependencies and temporal changes between research topics, we
propose a deep neural network-based research topic hotness prediction
algorithm, a spatiotemporal convolutional network model. Our model combines a
graph convolutional neural network (GCN) and Temporal Convolutional Network
(TCN), specifically, GCNs are used to learn the spatial dependencies of
research topics a and use space dependence to strengthen spatial
characteristics. TCN is used to learn the dynamics of research topics' trends.
Optimization is based on the calculation of weighted losses based on time
distance. Compared with the current mainstream sequence prediction models and
similar spatiotemporal models on the paper datasets, experiments show that, in
research topic prediction tasks, our model can effectively capture
spatiotemporal relationships and the predictions outperform state-of-art
baselines.

利用深度学习神经网络结合 GCN 和 TCN 的卷积网络模型提出一种基于历史数据的科研主题热度预测算法，通过捕捉主题之间的空间依存性和时间变化关系， 实现对未来研究热点的有效预测。