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 …

Although human action anticipation is a task which is inherently multi-modal, state-of-the-art
methods on well known action anticipation datasets leverage this data by applying …

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 …

Action anticipation involves predicting future actions having observed the initial portion of a
video. Typically, the observed video is processed as a whole to obtain a video-level …

We study the problem of future step anticipation in procedural videos. Given a video of an
ongoing procedural activity, we predict a plausible next procedure step described in rich …

Human-object interaction (HOI) and temporal dynamics along the motion paths are the most
important visual cues for egocentric action anticipation. Especially, interaction regions …

Accident anticipation attempts to predict whether an accident may occur in advance, which is
greatly significant for improving the safety of intelligent vehicles. Most existing approaches …

With the assumption that a video dataset is multimodality annotated in which auditory and
visual modalities both are labeled or class-relevant, current multimodal methods apply …

The problem of anticipating human actions is an inherently uncertain one. However, we can
reduce this uncertainty if we have a sense of the goal that the actor is trying to achieve. Here …

Egocentric action anticipation aims to predict the future actions the camera wearer will
perform from the observation of the past. While predictions about the future should be …