The ability to predict, anticipate and reason about future outcomes is a key component of
intelligent decision-making systems. In light of the success of deep learning in computer …

Abstract We propose Anticipative Video Transformer (AVT), an end-to-end attention-based
video modeling architecture that attends to the previously observed video in order to …

Future prediction is a fundamental principle of intelligence that helps plan actions and avoid
possible dangers. As the future is uncertain to a large extent, modeling the uncertainty and …

Human motion prediction, the task of predicting future 3D human poses given a sequence of
observed ones, has been mostly treated as a deterministic problem. However, human …

Computer vision systems face significant challenges in human-human interaction
recognition in dynamic environments, particularly with cluttered backgrounds. In this paper …

Multi-agent trajectory forecasting in autonomous driving requires an agent to accurately
anticipate the behaviors of the surrounding vehicles and pedestrians, for safe and reliable …

We propose a framework for early action recognition and anticipation by correlating past
features with the future using three novel similarity measures called Jaccard vector similarity …

Anticipating actions before they are executed is crucial for a wide range of practical
applications, including autonomous driving and robotics. In this paper, we study the …

To understand human to human dealing accurately, human interaction recognition (HIR)
systems require robust feature extraction and selection methods based on vision sensors. In …

We present RAFTformer, a real-time action forecasting transformer for latency aware real-
world action forecasting applications. RAFTformer is a two-stage fully transformer based …