Human behavior has the nature of indeterminacy, which requires the pedestrian trajectory
prediction system to model the multi-modality of future motion states. Unlike existing …

The performance of text-video retrieval has been significantly improved by vision-language
cross-modal learning schemes. The typical solution is to directly align the global video-level …

Pedestrian trajectory prediction is an essential component in a wide range of AI applications
such as autonomous driving and robotics. Existing methods usually assume the training and …

Capturing high-dimensional social interactions and feasible futures is essential for
predicting trajectories. To address this complex nature, several attempts have been devoted …

Pedestrian trajectory prediction is critical in many vision tasks but challenging due to the
multimodality of the future trajectory. Most existing methods predict multimodal trajectories …

Forecasting the future trajectory of pedestrians is an important task in computer vision with a
range of applications, from security cameras to autonomous driving. It is very challenging …

Self-supervised learning (SSL) as an effective paradigm of representation learning has
achieved tremendous success on various curated datasets in diverse scenarios …

We propose a novel framework for multi-person 3D motion trajectory prediction. Our key
observation is that a human's action and behaviors may highly depend on the other persons …

Modeling the dynamics of people walking is a problem of long-standing interest in computer
vision. Many previous works involving pedestrian trajectory prediction define a particular set …

Trajectory prediction has been a long-standing problem in intelligent systems like
autonomous driving and robot navigation. Models trained on large-scale benchmarks have …