This paper investigates traffic forecasting, which attempts to forecast the
future state of traffic based on historical situations. This problem has
received ever-increasing attention in various scenarios and facilitated the
development of numerous downstream applications such as urban planning and
transportation management. However, the efficacy of existing methods remains
sub-optimal due to their tendency to model temporal and spatial relationships
independently, thereby inadequately accounting for complex high-order
interactions of both worlds. Moreover, the diversity of transitional patterns
in traffic forecasting makes them challenging to capture for existing
approaches, warranting a deeper exploration of their diversity. Toward this
end, this paper proposes Conjoint Spatio-Temporal graph neural network
(abbreviated as COOL), which models heterogeneous graphs from prior and
posterior information to conjointly capture high-order spatio-temporal
relationships. On the one hand, heterogeneous graphs connecting sequential
observation are constructed to extract composite spatio-temporal relationships
via prior message passing. On the other hand, we model dynamic relationships
using constructed affinity and penalty graphs, which guide posterior message
passing to incorporate complementary semantic information into node
representations. Moreover, to capture diverse transitional properties to
enhance traffic forecasting, we propose a conjoint self-attention decoder that
models diverse temporal patterns from both multi-rank and multi-scale views.
Experimental results on four popular benchmark datasets demonstrate that our
proposed COOL provides state-of-the-art performance compared with the
competitive baselines.

本文介绍了交通预测的问题，并针对现有方法在建模时间和空间关系上的局限性以及对过渡模式的多样性的挑战，提出了一种名为 COOL 的综合时空图神经网络，通过构建异质图来捕捉高阶时空关系，并使用亲和力图和惩罚图来引导后续信息传递，进而增强交通预测的能力，并通过实验结果表明，COOL 相较竞争方法具有最先进的性能。

COOL：基于时空图神经网络的交通预测的共有观点

COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for  Traffic Forecasting

Recently, Space-Time Memory Network (STM) based methods have achieved
state-of-the-art performance in semi-supervised video object segmentation
(VOS). A crucial problem in this task is how to model the dependency both among
different frames and inside every frame. However, most of these methods neglect
the spatial relationships (inside each frame) and do not make full use of the
temporal relationships (among different frames). In this paper, we propose a
new transformer-based framework, termed TransVOS, introducing a vision
transformer to fully exploit and model both the temporal and spatial
relationships. Moreover, most STM-based approaches employ two separate encoders
to extract features of two significant inputs, i.e., reference sets (history
frames with predicted masks) and query frame (current frame), respectively,
increasing the models' parameters and complexity. To slim the popular
two-encoder pipeline while keeping the effectiveness, we design a single
two-path feature extractor to encode the above two inputs in a unified way.
Extensive experiments demonstrate the superiority of our TransVOS over
state-of-the-art methods on both DAVIS and YouTube-VOS datasets.

本文提出了一种新的基于 transformer 的框架 TransVOS，利用视觉 transformer 来充分利用和建模时间和空间关系，在保持效果的同时减少了模型参数和复杂性，从而实现了半监督视频对象分割中的最新性能。