The technology of traffic flow forecasting plays an important role in
intelligent transportation systems. Based on graph neural networks and
attention mechanisms, most previous works utilize the transformer architecture
to discover spatiotemporal dependencies and dynamic relationships. However,
they have not considered correlation information among spatiotemporal sequences
thoroughly. In this paper, based on the maximal information coefficient, we
present two elaborate spatiotemporal representations, spatial correlation
information (SCorr) and temporal correlation information (TCorr). Using SCorr,
we propose a correlation information-based spatiotemporal network (CorrSTN)
that includes a dynamic graph neural network component for integrating
correlation information into spatial structure effectively and a multi-head
attention component for modeling dynamic temporal dependencies accurately.
Utilizing TCorr, we explore the correlation pattern among different periodic
data to identify the most relevant data, and then design an efficient data
selection scheme to further enhance model performance. The experimental results
on the highway traffic flow (PEMS07 and PEMS08) and metro crowd flow (HZME
inflow and outflow) datasets demonstrate that CorrSTN outperforms the
state-of-the-art methods in terms of predictive performance. In particular, on
the HZME (outflow) dataset, our model makes significant improvements compared
with the ASTGNN model by 12.7%, 14.4% and 27.4% in the metrics of MAE, RMSE and
MAPE, respectively.

本文提出了基于最大信息系数的空间 / 时间相关性表示方法，利用该方法构建了一个基于相关信息的时空网络（CorrSTN），包括动态图神经网络和多头注意力，并使用这个网络在公路和地铁数据集上进行道路和人群的流量预测，结果表明 CorrSTN 在所有度量标准下均优于当前最先进的方法。

基于相关信息的时空网络在交通流量预测中的应用

A Correlation Information-based Spatiotemporal Network for Traffic Flow Forecasting

Correctly perceiving micro-expression is difficult since micro-expression is
an involuntary, repressed, and subtle facial expression, and efficiently
revealing the subtle movement changes and capturing the significant segments in
a micro-expression sequence is the key to micro-expression recognition (MER).
To handle the crucial issue, in this paper, we firstly propose a dynamic
segmented sparse imaging module (DSSI) to compute dynamic images as
local-global spatiotemporal descriptors under a unique sampling protocol, which
reveals the subtle movement changes visually in an efficient way. Secondly, a
segmented movement-attending spatiotemporal network (SMA-STN) is proposed to
further unveil imperceptible small movement changes, which utilizes a
spatiotemporal movement-attending module (STMA) to capture long-distance
spatial relation for facial expression and weigh temporal segments. Besides, a
deviation enhancement loss (DE-Loss) is embedded in the SMA-STN to enhance the
robustness of SMA-STN to subtle movement changes in feature level. Extensive
experiments on three widely used benchmarks, i.e., CASME II, SAMM, and SHIC,
show that the proposed SMA-STN achieves better MER performance than other
state-of-the-art methods, which proves that the proposed method is effective to
handle the challenging MER problem.

本篇论文提出动态分段稀疏成像模块（DSSI）和分段运动关注空间时间网络（SMA-STN）来处理微表情识别（MER）问题，通过计算局部 - 全局的时空描述符来捕捉微小运动变化，利用空 - 时运动关注模块（STMA）来捕捉面部表情的远程空间关系和加权时间段，实验证明该方法优于其他先进方法，证明该方法是解决复杂的 MER 问题有效的。

SMA-STN: 分段运动关注的时空网络用于微表情识别

SMA-STN: Segmented Movement-Attending Spatiotemporal Network  forMicro-Expression Recognition

The current main stream methods formulate their video saliency mainly from
two independent venues, i.e., the spatial and temporal branches. As a
complementary component, the main task for the temporal branch is to
intermittently focus the spatial branch on those regions with salient
movements. In this way, even though the overall video saliency quality is
heavily dependent on its spatial branch, however, the performance of the
temporal branch still matter. Thus, the key factor to improve the overall video
saliency is how to further boost the performance of these branches efficiently.
In this paper, we propose a novel spatiotemporal network to achieve such
improvement in a full interactive fashion. We integrate a lightweight temporal
model into the spatial branch to coarsely locate those spatially salient
regions which are correlated with trustworthy salient movements. Meanwhile, the
spatial branch itself is able to recurrently refine the temporal model in a
multi-scale manner. In this way, both the spatial and temporal branches are
able to interact with each other, achieving the mutual performance improvement.
Our method is easy to implement yet effective, achieving high quality video
saliency detection in real-time speed with 50 FPS.

本文提出了一种新颖的时空网络来提高视频显著性检测的性能，通过将轻量级的时间模型整合到空间分支中，粗略定位与值得信赖的显著运动相关的空间显著区域，同时，空间分支本身能够多尺度地反复改善时间模型，从而使空间和时间分支能够互相交互，实现相互性能提高。我们的方法易于实现且有效，在实时速度下以 50 FPS 实现高质量的视频显著性检测。