Spatial-Temporal Graph (STG) data is characterized as dynamic, heterogenous,
and non-stationary, leading to the continuous challenge of spatial-temporal
graph learning. In the past few years, various GNN-based methods have been
proposed to solely focus on mimicking the relationships among node individuals
of the STG network, ignoring the significance of modeling the intrinsic
features that exist in STG system over time. In contrast, modern Selective
State Space Models (SSSMs) present a new approach which treat STG Network as a
system, and meticulously explore the STG system's dynamic state evolution
across temporal dimension. In this work, we introduce Spatial-Temporal Graph
Mamba (STG-Mamba) as the first exploration of leveraging the powerful selective
state space models for STG learning by treating STG Network as a system, and
employing the Graph Selective State Space Block (GS3B) to precisely
characterize the dynamic evolution of STG networks. STG-Mamba is formulated as
an Encoder-Decoder architecture, which takes GS3B as the basic module, for
efficient sequential data modeling. Furthermore, to strengthen GNN's ability of
modeling STG data under the setting of SSSMs, we propose Kalman Filtering Graph
Neural Networks (KFGN) for adaptive graph structure upgrading. KFGN smoothly
fits in the context of selective state space evolution, and at the same time
keeps linear complexity. Extensive empirical studies are conducted on three
benchmark STG forecasting datasets, demonstrating the performance superiority
and computational efficiency of STG-Mamba. It not only surpasses existing
state-of-the-art methods in terms of STG forecasting performance, but also
effectively alleviate the computational bottleneck of large-scale graph
networks in reducing the computational cost of FLOPs and test inference time.

基于选择性状态空间模型，该研究提出了一种名为 STG-Mamba 的时空图学习方法，借助图选择性状态空间块（GS3B）刻画了 STG 网络的动态演化，并引入了 Kalman 滤波图神经网络（KFGN）来提升 GNN 在 STG 数据建模中的能力。大量实证研究表明了 STG-Mamba 在预测性能和计算效率方面的优越性。

STG-Mamba: 通过选择性状态空间模型进行时空图学习

STG-Mamba: Spatial-Temporal Graph Learning via Selective State Space  Model

Video-based person re-identification (re-ID) is an important technique in
visual surveillance systems which aims to match video snippets of people
captured by different cameras. Existing methods are mostly based on
convolutional neural networks (CNNs), whose building blocks either process
local neighbor pixels at a time, or, when 3D convolutions are used to model
temporal information, suffer from the misalignment problem caused by person
movement. In this paper, we propose to overcome the limitations of normal
convolutions with a human-oriented graph method. Specifically, features located
at person joint keypoints are extracted and connected as a spatial-temporal
graph. These keypoint features are then updated by message passing from their
connected nodes with a graph convolutional network (GCN). During training, the
GCN can be attached to any CNN-based person re-ID model to assist
representation learning on feature maps, whilst it can be dropped after
training for better inference speed. Our method brings significant improvements
over the CNN-based baseline model on the MARS dataset with generated person
keypoints and a newly annotated dataset: PoseTrackReID. It also defines a new
state-of-the-art method in terms of top-1 accuracy and mean average precision
in comparison to prior works.

本文提出了一种基于人体关节特征的人物重识别方法，通过采用图卷积网络与卷积神经网络相结合的方式解决了运动带来的误差问题。在 MARS 数据集和 PoseTrackReID 数据集上，该方法在 top-1 准确性和平均精度方面均优于前人工作，定义了新的最先进方法。

基于关键点消息传递的视频行人重识别

Keypoint Message Passing for Video-based Person Re-Identification

Predicting the future paths of an agent's neighbors accurately and in a
timely manner is central to the autonomous applications for collision
avoidance. Conventional approaches, e.g., LSTM-based models, take considerable
computational costs in the prediction, especially for the long sequence
prediction. To support more efficient and accurate trajectory predictions, we
propose a novel CNN-based spatial-temporal graph framework GraphTCN, which
models the spatial interactions as social graphs and captures the
spatio-temporal interactions with a modified temporal convolutional network. In
contrast to conventional models, both the spatial and temporal modeling of our
model are computed within each local time window. Therefore, it can be executed
in parallel for much higher efficiency, and meanwhile with accuracy comparable
to best-performing approaches. Experimental results confirm that our model
achieves better performance in terms of both efficiency and accuracy as
compared with state-of-the-art models on various trajectory prediction
benchmark datasets.

本研究提出了基于 CNN 的时空图形框架 GraphTCN，通过将空间交互作为社交图形进行建模，并使用修改的时间卷积网络来捕捉时空交互。实验结果证实，该模型在各种轨迹预测基准数据集上实现了更高的性能和更高的准确性。