Missing values, which are common in multivariate time series, is most important obstacle
towards the utilization and interpretation of those data. Great efforts have been employed on …

Modelling spatio-temporal processes on road networks is a task of growing importance.
While significant progress has been made on developing spatio-temporal graph neural …

With the progress of urban transportation systems, a significant amount of high-quality traffic
data is continuously collected through streaming manners, which has propelled the …

Spatio-temporal prediction plays a critical role in smart city construction. Jointly modeling
multiple spatio-temporal tasks can further promote an intelligent city life by integrating their …

We study the task of spatio-temporal extrapolation that generates data at target locations
from surrounding contexts in a graph. This task is crucial as sensors that collect data are …

Spatiotemporal data forecasting is a fundamental task in the field of graph data mining.
Typical spatiotemporal data prediction methods usually capture spatial dependencies by …

Fusing multimodal heterogeneous data plays a vital role in recognition and prediction tasks
in various fields, eg, action recognition and traffic accident forecast. Yet, there remain some …

Currently, most Discriminative Correlation Filters (DCF) algorithms used for Unmanned
Aerial Vehicle (UAV) target tracking primarily focus on improving the tracking model …

Traffic prediction is one of the important research directions in Intelligent Transportation
Systems, with positive implications for vehicle dispatching and vehicle management. In …

Graph Neural Networks (GNNs) can learn representative graph-level features to achieve
efficient graph classification. But GNNs usually assume an environment where both class …