Action anticipation is the task of forecasting future activity from a partially observed
sequence of events. However, this task is exposed to intrinsic future uncertainty and the …

Trajectory prediction is an important task that involves modeling the indeterminate nature of
agents to forecast future trajectories given the observed trajectory sequences. The task of …

A graph neural network (GNN) is a powerful architecture for semi-supervised learning (SSL).
However, the data-driven mode of GNNs raises some challenging problems. In particular …

Pedestrian trajectory prediction holds significant research value in various fields, such as
autonomous driving, autonomous service robots, and human flow monitoring. Two key …

Pedestrian trajectory prediction is critical for understanding human behavior. The prevailing
approaches employ neural networks to predict trajectories from large amounts of trajectory …

Trajectory prediction aims to forecast agents' possible future locations considering their
observations along with the video context. It is strongly needed by many autonomous …

Accurate prediction of vehicle trajectories is crucial to the safety and comfort of autonomous
vehicles. Although several graph-based models have exhibited substantial progress in …

Pedestrian trajectory prediction in first-person view is an important support for achieving fully
automated driving in cities. However, existing pedestrian trajectory prediction methods still …

Neuroevolution has greatly promoted Deep Neural Network (DNN) architecture design and
its applications, while there is a lack of methods available across different DNN types …

Pedestrian trajectory prediction holds significant research value in service robots,
autonomous driving, and intelligent monitoring. Currently, most pedestrian trajectory …